The introduction of big data analytics in physical education gives a new approach to individual learning, especially university level in badminton. This study examined the effects of data-driven instruction, adaptive training plans, performance assessment and monitoring, video-based performance, and real-time adaptive coaching on student performance. The study used a quasi-experimental design, which included 368 students, 86 coaches, and the information was collected through the use of questionnaires, sensors, and motion capture devices. The analysis of statistics also showed significant changes in psychomotor, cognitive, and motivational areas. The most important effects were those of data-driven teaching and real-time adaptive coaching, as well as the continuous development of skills with the help of continuous monitoring. The results indicate that AI-based methods and big data can be effective and not too expensive to increase the performance of badminton in higher education.

Permission checks play an essential role in operating system security by providing access control to privileged functionalities. However, it is challenging for kernel developers to scalably verify the soundness of existing checks due to the large codebase and complexity of the kernel. In fact, Linux kernel contains millions of lines of code with hundreds of permission checks, and even worse its complexity is fast-growing. This paper presents PeX, a static permission check error detector for Linux, which takes as input a kernel source code and reports any missing, inconsistent, and redundant permission checks. PeX uses KIRIN (Kernel InteRface based Indirect call aNalysis), a novel, precise, and scalable indirect call analysis technique. Over the interprocedural control flow graph built by KIRIN, PeX automatically identifies permission checks and infers the mappings between permission checks and privileged functions. For each privileged function, PeX examines all possible paths to the function to check if necessary permission checks are correctly enforced. We evaluated PeX on the latest stable Linux kernel v4.18.5 for three types of permission checks: Discretionary Access Controls (DAC), Capabilities, and Linux Security Modules (LSM). PeX reported 45 new permission check errors, 17 of which have been confirmed by the kernel developers.

Binary-only bug search has already drawn a lot attentions recently, due to the increasing growth of security breaches. Most of existing work focuses on searching by checking the similarity of code snippets. It is further required to check whether the function is patched or not. Unfortunately, this is still a manual effort for all existing code search based approaches. In this paper, we propose a novel approach for automatic patch detection. we build a patch detector by learning the feature representation from the patched code in the binary format. We utilize the feature encoding technique to make the binary code trainable, and build our neural network model to learn the patch feature for increasing detection accuracy. We have implemented a prototype called PATCHDETECTOR, and systematically evaluated its performance in terms of the accuracy and efficiency by using 1,600 OpenSSL binaries of 216,000 functions. Experimental results have shown that PATCHDETECTOR can effectively detect whether the target binary function is patched or not with the detection accuracy of 92% on average.

Commodity operating systems kernels are typically implemented using low-level unsafe languages, which leads to the inevitability of memory corruption vulnerabilities. Multiple defense techniques are widely adopted to mitigate the impact of memory corruption on executable code and control data. Nevertheless, there has not been much attention to defend against corruption of non-control data despite the fact that previous incidents of kernel exploitation showed that corrupting non-control data is a real threat.

SEAndroid is a mandatory access control (MAC) framework that can confine faulty applications on Android. Nevertheless, the effectiveness of SEAndroid enforcement depends on the employed policy. The growing complexity of Android makes it difficult for policy engineers to have complete domain knowledge on every system functionality. As a result, policy engineers sometimes craft over-permissive and ineffective policy rules, which unfortunately increased the attack surface of the Android system and have allowed multiple real-world privilege escalation attacks. We propose SPOKE, an SEAndroid Policy Knowledge Engine, that systematically extracts domain knowledge from rich-semantic functional tests and further uses the knowledge for characterizing the attack surface of SEAndroid policy rules. Our attack surface analysis is achieved by two steps: 1) It reveals policy rules that cannot be justified by the collected domain knowledge. 2) It identifies potentially over-permissive access patterns allowed by those unjustified rules as the attack surface.

In various resource sharing networks, opportunistic resources with dynamic quality are often present for the users to exploit. As many user tasks are delay-tolerant, this favorably allows the network users to wait for and access the opportunistic resource at the time of its best quality. For such delay-tolerant and opportunistic resource sharing networks, the resource accessing strategies developed in the literature suffer from three limitations. First, they mainly focused on single-user scenarios, whereas the competition from other users is ignored. Second, the influence from the resource seller who may take actions to manipulate the resource sharing procedure is not considered. Third, the impact of the actions from both the network users and the resource seller on the resource quality dynamics is not considered either. To overcome these limitations, a leader-follower controlled Markov stopping game (LF-C-MSG) is developed in this paper. The derived Stackelberg equilibrium strategy of the LF-C-MSG can be used to guide the behaviors of both the network users and the resource seller for better performance and resource utilization efficiency. Two exemplary applications of the proposed LF-C-MSG are presented, along with corresponding numerical results to verify the effectiveness of the proposed framework.

Opportunistic resources are often present in various resource sharing networks for the users to exploit, but their qualities often change over time. Fortunately, many user tasks are delay-tolerant, which offers the network users a favorable degree of freedom in waiting for and accessing the opportunistic resource at the time of its best quality. For such delay-tolerant and opportunistic resource sharing networks (DT-ORS-Net), the corresponding optimal accessing strategies developed in existing literature mainly focus on the single-user scenarios, while the potential competition from other peer users in practical multi-user DT-ORS-Net is often ignored. Considering this, a multi-player Markov stopping game (M-MSG) is developed in this work, and the derived Nash equilibrium (NE) strategy of this M-MSG can guide network users to properly handle the potential competition from other peers and thus exploit the time diversity of the opportunistic resource more effectively, which in turn further improves the resource utilization efficiency. Applications in the cloud-computing and the mobile crowdsourcing networks are demonstrated to verify the effectiveness of the proposed method, and simulation results show that using the NE strategy of the proposed M-MSG can provide substantial performance gain as compared to using the conventional single-user optimal one.

Zero-determinant (ZD) strategies that allow a player to unilaterally control the linear combinations of its own and other players' expected rewards in iterated games have recently found wide applications. However, existing ZD strategies mainly focus on some specific scenarios with restrictions on the number of players or actions a player can take. Targeting wider applications and better performance, the ZD strategies along with corresponding existence conditions for general multi-player multi-action iterated games are developed in this work, including existing ones as special cases. In addition, an interesting fact that every player can have at most one master player (that can control the expected reward of the given player) is revealed.

A reactive jammer jams wireless channels only when target devices are transmitting; Compared to constant jamming, reactive jamming is harder to track and compensate against [2], [38]. Frequency hopping spread spectrum (FHSS) and direct sequence spread spectrum (DSSS) have been widely used as countermeasures against jamming attacks. However, both will fail if the jammer jams all frequency channels or has high transmit power. In this paper, we propose an anti-jamming communication system that allows communication in the presence of a broadband and high power reactive jammer. The proposed system transmits messages by harnessing the reaction time of a reactive jammer. It does not assume a reactive jammer with limited spectrum coverage and transmit power, and thus can be used in scenarios where traditional approaches fail. We develop a prototype of the proposed system using GNURadio. Our experimental evaluation shows that when a powerful reactive jammer is present, the prototype still keeps communication, whereas other schemes such as 802.11 DSSS fail completely.

Wireless link signature is a physical layer authentication mechanism, using the multipath effect between a transmitter and a receiver to provide authentication of wireless signals. This paper identifies a new attack, called mimicry attack, against the existing wireless link signature schemes. An attacker can forge a legitimate transmitter's link signature as long as it knows the legitimate signal at the receiver's location, and the attacker does not have to be at exactly the same location as the legitimate transmitter. We also extend the mimicry attack to multiple-input multiple-output (MIMO) systems, and conclude that the mimicry attack is feasible only when the number of attacker' antennas is equal to or larger than that of the receiver's antennas. To defend against the mimicry attack, this paper proposes a novel construction for wireless link signature, called time-synched link signature, by integrating cryptographic protection and time factor into wireless physical layer features. Experimental results confirm that the mimicry attack is a real threat and the newly proposed time-synched link signatures are effective in physical layer authentication.

Motivated by information-theoretic security, link signature (LS)-based security mechanisms exploit the ample channel characteristics between wireless devices for security establishment. Nevertheless, LS is originated from wireless environments and hence may exhibit potential vulnerabilities that can be exploited by adversary in the vicinity. As to this, it is widely believed in existing literature on LS that, a half-wavelength guard zone is sufficient to decorrelate the adversary channel from the legitimate one and thereby secures the legitimate LS. However, such an assumption may not hold universally - in some environments, high channel correlations have been observed for much larger spatial separations. Considering this, a comprehensive understanding of channel correlation in different wireless environments is needed for more confident deployment of LS-based security mechanisms. To this end, various well-established channel correlation models are investigated in this work. A set of important physical factors that have significant influence on LS security are identified, and with the obtained insights, extensive simulations are conducted to explore suitable guard zone sizes for LS in several typical indoor and outdoor environments. Experimental results based on universal software radio peripheral (USRP) platforms and GNURadio are also presented to further support the analysis.

Memory disclosure vulnerabilities have become a common component for enabling reliable exploitation of systems by leaking the contents of executable data. Previous research towards protecting executable data from disclosure has failed to gain popularity due to large performance penalties and required architectural changes. Other research has focused on protecting application data but fails to consider a vulnerable application that leaks its own executable data.

Intrusion detection systems (IDSs) assume increasingly importance in past decades as information systems become ubiquitous. Despite the abundance of intrusion detection algorithms developed so far, there is still no single detection algorithm or procedure that can catch all possible intrusions; also, simultaneously running all these algorithms may not be feasible for practical IDSs due to resource limitation. For these reasons, effective IDS configuration becomes crucial for real-time intrusion detection. However, the uncertainty in the intruder's type and the (often unknown) dynamics involved with the target system pose challenges to IDS configuration. Considering these challenges, the IDS configuration problem is formulated as an incomplete information stochastic game in this work, and a new algorithm, Bayesian Nash-Q learning, that combines conventional reinforcement learning with a Bayesian type identification procedure is proposed. Numerical results show that the proposed algorithm can identify the intruder's type with high fidelity and provide effective configuration.

To ensure continuous functioning and satisfactory performance, a wireless communication system has to not only learn and adapt to the unknown and ever-changing wireless environment, but also strategically deal with the usually unfamiliar peers. Incomplete information stochastic game (SG) is a promising model for the corresponding analysis and strategy design. In this work, an exemplary multi-channel spectrum access game (SAG) with unknown environment dynamics and limited information of the other player is considered to illustrate the proposed solution for the corresponding incomplete information SG. To find the best communication strategy in the face of uncertainty, a joint reinforcement learning and type identification algorithm is developed, which is provably convergent under certain technical conditions. Numerical results show that using the proposed algorithm, a wireless user can gradually achieve the same performance as that in the corresponding complete information game.

With the advancement of modern technologies, the security battle between a legitimate system (LS) and an adversary is becoming increasingly sophisticated, involving complex interactions in unknown dynamic environments. Stochastic game (SG), together with multi-agent reinforcement learning (MARL), offers a systematic framework for the study of information warfare in current and emerging cyber-physical systems. In practical security games, each player usually has only incomplete information about the opponent, which induces information asymmetry. This work exploits information asymmetry from a new angle, considering how to exploit local information unknown to the opponent to the player's advantage. Two new MARL algorithms, termed minimax-PDS and WoLF-PDS, are proposed, which enable the LS to learn and adapt faster in dynamic environments by exploiting its private local information. The proposed algorithms are provably convergent and rational, respectively. Also, numerical results are presented to show their effectiveness through two concrete anti-jamming examples.

Virus Scanning-as-a-Service (VSaaS) has emerged as a popular security solution for virtual cloud environments. However, existing approaches fail to scan guest memory, which can contain an emerging class of Memory-only Malware. While several host-based memory scanners are available, they are computationally less practical for cloud environments. This paper proposes SEER as an architecture for enabling Memory VSaaS for virtualized environments. SEER leverages cloud resources and technologies to consolidate and aggregate virus scanning activities to efficiently detect malware residing in memory. Specifically, SEER combines fast memory snapshotting and computation deduplication to provide practical and efficient off-host memory virus scanning. We evaluate SEER and demonstrate up to an 87% reduction in data size that must be scanned and up to 72% savings in overall scan time, compared to naively applying file-based scanning approaches. Furthermore, SEER provides a 50% reduction in scan time when using a warm cache. In doing so, SEER provides a practical solution for cloud vendors to transparently and periodically scan virtual machine memory for malware.

TrustZone-based Real-time Kernel Protection (TZ-RKP) is a novel system that provides real-time protection of the OS kernel using the ARM TrustZone secure world. TZ-RKP is more secure than current approaches that use hypervisors to host kernel protection tools. Although hypervisors provide privilege and isolation, they face fundamental security challenges due to their growing complexity and code size. TZ-RKP puts its security monitor, which represents its entire Trusted Computing Base (TCB), in the TrustZone secure world; a safe isolated environment that is dedicated to security services. Hence, the security monitor is safe from attacks that can potentially compromise the kernel, which runs in the normal world. Using the secure world for kernel protection has been crippled by the lack of control over targets that run in the normal world. TZ-RKP solves this prominent challenge using novel techniques that deprive the normal world from the ability to control certain privileged system functions. These functions are forced to route through the secure world for inspection and approval before being executed. TZ-RKP's control of the normal world is non-bypassable. It can effectively stop attacks that aim at modifying or injecting kernel binaries. It can also stop attacks that involve modifying the system memory layout, e.g, through memory double mapping. This paper presents the implementation and evaluation of TZ-RKP, which has gone through rigorous and thorough evaluation of effectiveness and performance. It is currently deployed on the latest models of the Samsung Galaxy series smart phones and tablets, which clearly demonstrates that it is a practical real-world system.

The seven papers in this special section explores issues relating to network and computer security as the Internet is becoming more ubiquitous. One central element of this trend is the existence of a massive network of interconnected wired/wireless physical objects, things, sensors, and devices, which can interact in a rich set of manners through a worldwide communication and information infrastructure to provide value added services. These papers present the most recent advances in IoT security.

The android platform adopts permissions to protect sensitive resources from untrusted apps. However, after permissions are granted by users at install time, apps could use these permissions (sensitive resources) with no further restrictions. Thus, recent years have witnessed the explosion of undesirable behaviors in Android apps. An important part in the defense is the accurate analysis of Android apps. However, traditional syscall-based analysis techniques are not well-suited for Android, because they could not capture critical interactions between the application and the Android system. This paper presents VetDroid, a dynamic analysis platform for generally analyzing sensitive behaviors in Android apps from a novel permission use perspective. VetDroid proposes a systematic permission use analysis technique to effectively construct permission use behaviors, i.e., how applications use permissions to access (sensitive) system resources, and how these acquired permission-sensitive resources are further utilized by the application. With permission use behaviors, security analysts can easily examine the internal sensitive behaviors of an app. Using real-world Android malware, we show that VetDroid can clearly reconstruct fine-grained malicious behaviors to ease malware analysis. We further apply VetDroid to 1249 top free apps in Google Play. VetDroid can assist in finding more information leaks than TaintDroid, a state-of-the-art technique. In addition, we show how we can use VetDroid to analyze fine-grained causes of information leaks that TaintDroid cannot reveal. Finally, we show that VetDroid can help to identify subtle vulnerabilities in some (top free) applications otherwise hard to detect.

In this paper, the mobility of network nodes is explored as a new promising approach for jamming defense. To fulfill it, properly designed node motion that can intelligently adapt to the jammer's action is crucial. In our study, anti-jamming mobility control is investigated in the context of the single and multiple commodity flow problems, in the presence of intelligent mobile jammers which can respond to the evasion of legitimate nodes as well. Based on spectral graph theory, two new spectral quantities, single- and multi-weighted Cheeger constants and corresponding eigenvalue variants, are constructed to direct motions of the defender and the attacker in this dynamic adaptive competition. Both analytical and simulation results are presented to justify the effectiveness of the proposed approach. Furthermore, the proposed scheme can also be applied in cognitive radio networks to reconfigure the secondary users in the presence of mobile primary users.

Android phones often carry personal information, attracting malicious developers to embed code in Android applications to steal sensitive data. With known techniques in the literature, one may easily determine if sensitive data is being transmitted out of an Android phone. However, transmission of sensitive data in itself does not necessarily indicate privacy leakage; a better indicator may be whether the transmission is by user intention or not. When transmission is not intended by the user, it is more likely a privacy leakage. The problem is how to determine if transmission is user intended. As a first solution in this space, we present a new analysis framework called AppIntent. For each data transmission, AppIntent can efficiently provide a sequence of GUI manipulations corresponding to the sequence of events that lead to the data transmission, thus helping an analyst to determine if the data transmission is user intended or not. The basic idea is to use symbolic execution to generate the aforementioned event sequence, but straightforward symbolic execution proves to be too time-consuming to be practical. A major innovation in AppIntent is to leverage the unique Android execution model to reduce the search space without sacrificing code coverage. We also present an evaluation of AppIntent with a set of 750 malicious apps, as well as 1,000 top free apps from Google Play. The results show that AppIntent can effectively help separate the apps that truly leak user privacy from those that do not.

Android platform adopts permissions to protect sensitive resources from untrusted apps. However, after permissions are granted by users at install time, apps could use these permissions (sensitive resources) with no further restrictions. Thus, recent years have witnessed the explosion of undesirable behaviors in Android apps. An important part in the defense is the accurate analysis of Android apps. However, traditional syscall-based analysis techniques are not well-suited for Android, because they could not capture critical interactions between the application and the Android system.

A fundamental assumption of link signature based security mechanisms is that the wireless signals received at two locations separated by more than half a wavelength are essentially uncorrelated. However, it has been observed that in certain circumstances (e.g., with poor scattering and/or a strong line-of-sight (LOS) component), this assumption is invalid. In this paper, a Correlation ATtack (CAT) is proposed to demonstrate the potential vulnerability of the link signature based security mechanisms in such circumstances. Based on statistical inference, CAT explicitly exploits the spatial correlations to reconstruct the legitimate link signature from the observations of multiple adversary receivers deployed in vicinity. Our findings are verified through theoretical analysis, well-known channel correlation models, and experiments on USRP platforms and GNURadio.

This paper presents a novel mechanism, called Ally Friendly Jamming, which aims at providing an intelligent jamming capability that can disable unauthorized (enemy) wireless communication but at the same time still allow authorized wireless devices to communicate, even if all these devices operate at the same frequency. The basic idea is to jam the wireless channel continuously but properly control the jamming signals with secret keys, so that the jamming signals are unpredictable interference to unauthorized devices, but are recoverable by authorized ones equipped with the secret keys. To achieve the ally friendly jamming capability, we develop new techniques to generate ally jamming signals, to identify and synchronize with multiple ally jammers. This paper also reports the analysis, implementation, and experimental evaluation of ally friendly jamming on a software defined radio platform. Both the analytical and experimental results indicate that the proposed techniques can effectively disable enemy wireless communication and at the same time maintain wireless communication between authorized devices.

Collaborative spectrum sensing improves the spectrum state estimation accuracy but is vulnerable to the potential attacks from malicious secondary cognitive radio (CR) users, and thus raises security concerns. One promising malicious user detection method is to identify their abnormal statistical spectrum sensing behaviors. From this angle, two hidden Markov models (HMMs) corresponding to honest and malicious users respectively are adopted in this paper to characterize their different sensing behaviors, and malicious user detection is achieved via detecting the difference in the corresponding HMM parameters. To obtain the HMM estimates, an effective inference algorithm that can simultaneously estimate two HMMs without requiring separated training sequences is also developed. By using these estimates, high malicious user detection accuracy can be achieved at the fusion center, leading to more robust and reliable collaborative spectrum sensing performance (substantially enlarged operational regions) in the presence of malicious users, as compared to the baseline approaches. Different fusion methods are also discussed and compared.

Recent studies have demonstrated that it is feasible to perform public key cryptographic operations on resource-constrained sensor platforms. However, the significant energy consumption introduced by public key operations makes any public key-based protocol an easy target of Denial-of-Service (DoS) attacks. For example, if digital signature schemes such as ECDSA are used directly for broadcast authentication without further protection, an attacker can simply broadcast fake messages and force the receiving nodes to perform a huge number of unnecessary signature verifications, eventually exhausting their battery power. This paper shows how to mitigate such DoS attacks when digital signatures are used for broadcast authentication in sensor networks. Specifically, this paper first presents two filtering techniques, the group-based filter and the key chain-based filter , to handle the DoS attacks against signature verification. Both methods can significantly reduce the number of unnecessary signature verifications when a sensor node is under DoS attacks. This paper then combines these two filters and proposes a hybrid solution to further improve the performance.

Security concerns are raised for collaborative spectrum sensing due to its vulnerabilities to the potential attacks from malicious secondary users. Most existing malicious user detection methods are reputation-based, which become incapable when the malicious users dominate the network. On the other hand, although Markovian models characterize the spectrum state behavior more precisely, there is a scarcity of malicious user detection methods which fully explore this feature. In this paper, a new malicious user detection method using two proposed conditional frequency check (CFC) statistics is developed under the Markovian model for the spectrum state. With the assistance of one trusted user, the proposed method can achieve high malicious user detection accuracy (≥ 95%) for arbitrary percentage of malicious users that may even be equipped with more advanced sensing devices, and can thus improve the collaborative spectrum sensing performance significantly. Simulation results are provided to verify the theoretical analysis and effectiveness of the proposed method.

Recent years have witnessed incredible popularity and adoption of smartphones and mobile devices, which is accompanied by large amount and wide variety of feature-rich smartphone applications. These smartphone applications (or apps), typically organized in different application marketplaces, can be conveniently browsed by mobile users and then simply clicked to install on a variety of mobile devices. In practice, besides the official marketplaces from platform vendors (e.g., Google and Apple), a number of third-party alternative marketplaces have also been created to host thousands of apps (e.g., to meet regional or localization needs). To maintain and foster a hygienic smartphone app ecosystem, there is a need for each third-party marketplace to offer quality apps to mobile users.

Reactive jamming is not only cost effective, but also hard to track and remove due to its intermittent jamming behaviors. Frequency Hopping Spread Spectrum (FHSS) and Direct Sequence Spread Spectrum (DSSS) have been widely used to defend against jamming attacks. However, both will fail if the jammer jams all frequency channels or has high transmit power. In this paper, we propose BitTrickle, an anti-jamming wireless communication scheme that allows communication in the presence of a broadband and high power reactive jammer by exploiting the reaction time of the jammer. We develop a prototype of BitTrickle using the USRP platform running GNURadio. Our evaluation shows that when under powerful reactive jamming, BitTrickle still maintains communication, whereas other schemes such as 802.11 DSSS fail completely.

Wireless link signature is a physical layer authentication mechanism, which uses the unique wireless channel characteristics between a transmitter and a receiver to provide authentication of wireless channels. A vulnerability of existing link signature schemes has been identified by introducing a new attack, called mimicry attack. To defend against the mimicry attack, we propose a novel construction for wireless link signature, called time-synched link signature, by integrating cryptographic protection and time factor into traditional wireless link signatures. We also evaluate the mimicry attacks and the time-synched link signature scheme on the USRP2 platform running GNURadio. The experimental results demonstrate the effectiveness of time-synched link signature.

Enterprise networks today host a wide variety of network services, which often depend on each other to provide and support network-based services and applications. Understanding such dependencies is essential for maintaining the well-being of an enterprise network and its applications, particularly in the presence of network attacks and failures. In a typical enterprise network, which is complex and dynamic in configuration, it is non-trivial to identify all these services and their dependencies. Several techniques have been developed to learn such dependencies automatically. However, they are either too complex to fine tune or cluttered with false positives and/or false negatives. In this paper, we propose a suite of novel techniques and develop a new tool named NSDMiner (which stands for Mining for Network Service Dependencies) to automatically discover the dependencies between network services from passively collected network traffic. NSDMiner is non-intrusive; it does not require any modification of existing software, or injection of network packets. More importantly, NSDMiner achieves higher accuracy than previous network-based approaches. Our experimental evaluation, which uses network traffic collected from our campus network, shows that NSDMiner outperforms the two best existing solutions significantly.

Collaborative spectrum sensing is vulnerable to the Byzantine attack. Existing reputation based countermeasures will become incapable when malicious users dominate the network. Also, there is a scarcity of methods that fully explore the Markov property of the spectrum states to restrain sensors' statistical misbehaviors. In this paper, a new malicious user detection method based on two proposed Conditional Frequency Check (CFC) statistics is developed with a Markovian spectrum model. With the assistance of one trusted sensor, the proposed method can achieve high malicious user detection accuracy in the presence of arbitrary percentage of malicious users, and thus significantly improves collaborative spectrum sensing performance.

We propose a jamming-resistant collaborative broadcast scheme for wireless networks, which utilizes the Un coordinated Frequency Hopping (UFH) technique to counteract jamming without preshared keys, and exploits node cooperation to achieve higher communication efficiency and stronger jamming resistance. In this scheme, nodes that already obtain the broadcast message serve as relays to help forward it to other nodes. Relying on the sheer number of relay nodes, our scheme provides a new angle for jamming countermeasure, which not only significantly enhances the performance of jamming-resistant broadcast, but can readily be combined with other existing or emerging antijamming approaches in various applications. We present the collaborative broadcast protocol, and analyze its successful packet reception rate and the corresponding cooperation gain for both synchronous and asynchronous relays for a snapshot scenario. We also investigate the full broadcast process based on a Markov chain model and derive a closed-form expression of the average broadcast delay. Simulation results in both single-hop and multihop networks indicate that our scheme is a promising antijamming technique in wireless networks.

Audit logs are an integral part of modern computer systems due to their forensic value. Protecting audit logs on a physically unprotected machine in hostile environments is a challenging task, especially in the presence of active adversaries. It is critical for such a system to have forward security and append-only properties such that when an adversary compromises a logging machine, she cannot forge or selectively delete the log entries accumulated before the compromise. Existing public-key-based secure logging schemes are computationally costly. Existing symmetric secure logging schemes are not publicly verifiable and open to certain attacks. In this article, we develop a new forward-secure and aggregate signature scheme called Blind-Aggregate-Forward (BAF) , which is suitable for secure logging in resource-constrained systems. BAF is the only cryptographic secure logging scheme that can produce publicly verifiable, forward-secure and aggregate signatures with low computation, key/signature storage, and signature communication overheads for the loggers, without requiring any online trusted third party support . A simple variant of BAF also allows a fine-grained verification of log entries without compromising the security or computational efficiency of BAF. We prove that our schemes are secure in Random Oracle Model (ROM). We also show that they are significantly more efficient than all the previous publicly verifiable cryptographic secure logging schemes.

Broadcast is a critical communication primitive in wireless sensor networks. The multihop nature of sensor networks makes it necessary for sensor nodes to forward broadcast messages so that the messages can reach an entire network. Authentication of broadcast messages is an important but challenging problem in sensor networks. Public key cryptography (PKC) has been used recently to address this problem. However, PKC-based authentication techniques are susceptible to bogus packet insertion attacks in which attackers keep broadcasting bogus messages and force resource-constrained sensor nodes to forward such messages. Moreover, because it takes time to do signature verifications, it is impractical for each node to authenticate every received message before forwarding it. In this article, we propose a dynamic window scheme to thwart the aforementioned bogus packet insertion attacks which permits sensor nodes to efficiently broadcast messages. Within this scheme, a sensor node has the ability to determine whether or not to verify an incoming message before forwarding the message. We further study the property of this dynamic window scheme and investigate the best strategy for thwarting bogus packet insertion attacks. We propose three strategies for finding the optimal parameters by an improved additive increase multiplicative decrease (AIMD) window updating function so that the proposed dynamic window scheme can achieve the best overall performance with respect to the authentication and forwarding times of messages. Numerical validations show that our proposed scheme performs very well in terms of energy saving and broadcast delays based on three different metrics, including average authentication delays, the percentage of nodes receiving fake messages, and the percentage of nodes forwarding fake messages.

SICE is a novel framework to provide hardware-level isolation and protection for sensitive workloads running on x86 platforms in compute clouds. Unlike existing isolation techniques, SICE does not rely on any software component in the host environment (i.e., an OS or a hypervisor). Instead, the security of the isolated environments is guaranteed by a trusted computing base that only includes the hardware, the BIOS, and the System Management Mode (SMM). SICE provides fast context switching to and from an isolated environment, allowing isolated workloads to time-share the physical platform with untrusted workloads. Moreover, SICE supports a large range (up to 4GB) of isolated memory. Finally, the most unique feature of SICE is the use of multicore processors to allow the isolated environments to run concurrently and yet securely beside the untrusted host. We have implemented a SICE prototype using an AMD x86 hardware platform. Our experiments show that SICE performs fast context switching (67 microseconds) to and from the isolated environment and that it imposes a reasonable overhead (3% on all but one benchmark) on the operation of an isolated Linux virtual machine. Our prototype demonstrates that, subject to a careful security review of the BIOS software and the SMM hardware implementation, current hardware architecture already provides abstractions that can support building strong isolation mechanisms using a very small SMM software foundation of about 300 lines of code.

We propose in [1] a collaborative broadcast scheme for wireless networks, which applies the Uncoordinated Frequency Hopping (UFH) technique to counteract jamming and exploits node cooperation to enhance broadcast efficiency. In this scheme, some nodes that already obtain the broadcast message are selected to relay the message to other nodes. In this paper, we extend the study to the generalized multihop network scenarios, and provide solutions for important related issues, such as the relay node selection, multiple access control, relay channel selection and packet scheduling.We also study the spatial and frequency (channel) diversity provided by the collaborative broadcast. Simulation results show that the collaborative broadcast achieves low broadcast delay, with low energy consumption and small computational overhead in multihop networks.

We propose a collaborative broadcast scheme for wireless networks, which is based on the Uncoordinated Frequency Hopping (UFH) technique and exploits the node cooperation to achieve higher communication efficiency and stronger jamming resistance. In the collaborative broadcast, nodes that already obtain the broadcast message help forward the message to other nodes. Relying on the sheer number of relay nodes, which grows with time surely, our scheme is fundamentally more powerful than most recent attempts for anti-jamming broadcast. Potential applications include emergency alert broadcast and distribution of key system information in the presence of jamming. We provide three relay channel selection strategies for collaborative broadcast, analyze the corresponding successful packet reception rates for both synchronous and asynchronous scenarios, and present the corresponding cooperation gain. Simulation results in a practical setting show that our scheme significantly reduces broadcast delay and energy consumption against the most powerful jamming, - responsive-sweep jamming.

Recently, many opportunistic spectrum sensing and access protocols have been proposed for cognitive radio networks (CRNs). For achieving optimized spectrum usage, existing solutions model the spectrum sensing and access problem as a partially observed Markov decision process (POMDP) and assume that the information states and/or the primary users' (PUs) traffic statistics are known a priori to the secondary users (SUs). While theoretically sound, these existing approaches may not be effective in practice due to two main concerns. First, the assumptions they made are not practical, as before the communication starts, PUs' traffic statistics may not be readily available to the SUs. Secondly and more seriously, existing approaches are extremely vulnerable to malicious jamming attacks. A cognitive attacker can always jam the channels to be accessed by leveraging the same statistic information and stochastic dynamic decision making process that the SUs would follow. To address the above concerns, we formulate the problem of anti-jamming multichannel access in CRNs and solve it as a non-stochastic multi-armed bandit (NS-MAB) problem, where the secondary sender and receiver adaptively choose their arms (i.e., sending and receiving channels) to operate. The proposed protocol enables them to hop to the same set of channels with high probability in the presence of jamming. We analytically show the convergence of the learning algorithms, i.e., the performance difference between the secondary sende and receiver's optimal strategies is no more than O(20k/√ε √Tn ln n). Extensive simulations are conducted to validate the theoretical analysis and show that the proposed protocol is highly resilient to various jamming attacks.

Vehicular ad hoc networks (VANETs) are being increasingly advocated for traffic control, accident avoidance, and management of parking lots and public areas. Security and privacy are two major concerns in VANETs. Unfortunately, in VANETs, most privacy-preserving schemes are vulnerable to Sybil attacks, whereby a malicious user can pretend to be multiple (other) vehicles. In this paper, we present a lightweight and scalable protocol to detect Sybil attacks. In this protocol, a malicious user pretending to be multiple (other) vehicles can be detected in a distributed manner through passive overhearing by s set of fixed nodes called road-side boxes (RSBs). The detection of Sybil attacks in this manner does not require any vehicle in the network to disclose its identity; hence privacy is preserved at all times. Simulation results are presented for a realistic test case to highlight the overhead for a centralized authority such as the DMV, the false alarm rate, and the detection latency. The results also quantify the inherent trade-off between security, i.e., the detection of Sybil attacks and detection latency, and the privacy provided to the vehicles in the network. From the results, we see our scheme being able to detect Sybil attacks at low overhead and delay, while preserving privacy of vehicles.

A power grid is a complex system connecting electric power generators to consumers through power transmission and distribution networks across a large geographical area. System monitoring is necessary to ensure the reliable operation of power grids, and state estimation is used in system monitoring to best estimate the power grid state through analysis of meter measurements and power system models. Various techniques have been developed to detect and identify bad measurements, including interacting bad measurements introduced by arbitrary, nonrandom causes. At first glance, it seems that these techniques can also defeat malicious measurements injected by attackers. In this article, we expose an unknown vulnerability of existing bad measurement detection algorithms by presenting and analyzing a new class of attacks, called false data injection attacks , against state estimation in electric power grids. Under the assumption that the attacker can access the current power system configuration information and manipulate the measurements of meters at physically protected locations such as substations, such attacks can introduce arbitrary errors into certain state variables without being detected by existing algorithms. Moreover, we look at two scenarios, where the attacker is either constrained to specific meters or limited in the resources required to compromise meters. We show that the attacker can systematically and efficiently construct attack vectors in both scenarios to change the results of state estimation in arbitrary ways. We also extend these attacks to generalized false data injection attacks , which can further increase the impact by exploiting measurement errors typically tolerated in state estimation. We demonstrate the success of these attacks through simulation using IEEE test systems, and also discuss the practicality of these attacks and the real-world constraints that limit their effectiveness.

Spread spectrum techniques such as Direct Sequence Spread Spectrum (DSSS) and Frequency Hopping (FH) have been commonly used for anti-jamming wireless communication. However, traditional spread spectrum techniques require that sender and receivers share a common secret in order to agree upon, for example, a common hopping sequence (in FH) or a common spreading code sequence (in DSSS). Such a requirement prevents these techniques from being effective for anti-jamming broadcast communication, where a jammer may learn the key from a compromised receiver and then disrupt the wireless communication. In this paper, we develop a novel Delayed Seed-Disclosure DSSS (DSD-DSSS) scheme for efficient anti-jamming broadcast communication. DSD-DSSS achieves its anti-jamming capability through randomly generating the spreading code sequence for each message using a random seed and delaying the disclosure of the seed at the end of the message. We also develop an effective protection mechanism for seed disclosure using content-based code subset selection. DSD-DSSS is superior to all previous attempts for anti-jamming spread spectrum broadcast communication without shared keys. In particular, even if a jammer possesses real-time online analysis capability to launch reactive jamming attacks, DSD-DSSS can still defeat the jamming attacks with a very high probability. We evaluate DSD-DSSS through both theoretical analysis and a prototype implementation based on GNU Radio; our evaluation results demonstrate that DSD-DSSS is practical and have superior security properties.

Patching is a critical security service that keeps computer systems up to date and defends against security threats. Existing patching systems all require running systems. With the increasing adoption of virtualization and cloud computing services, there is a growing number of dormant virtual machine (VM) images. Such VM images cannot benefit from existing patching systems, and thus are often left vulnerable to emerging security threats. It is possible to bring VM images online, apply patches, and capture the VMs back to dormant images. However, such approaches suffer from unpredictability, performance challenges, and high operational costs, particularly in large-scale compute clouds where there could be thousands of dormant VM images.

This paper presents HyperSentry, a novel framework to enable integrity measurement of a running hypervisor (or any other highest privileged software layer on a system). Unlike existing solutions for protecting privileged software, HyperSentry does not introduce a higher privileged software layer below the integrity measurement target, which could start another race with malicious attackers in obtaining the highest privilege in the system. Instead, HyperSentry introduces a software component that is properly isolated from the hypervisor to enable stealthy and in-context measurement of the runtime integrity of the hypervisor. While stealthiness is necessary to ensure that a compromised hypervisor does not have a chance to hide the attack traces upon detecting an up-coming measurement, in-context measurement is necessary to retrieve all the needed inputs for a successful integrity measurement.

Jamming resistance is crucial for applications where reliable wireless communication is required. Spread spectrum techniques such as Frequency Hopping Spread Spectrum (FHSS) and Direct Sequence Spread Spectrum (DSSS) have been used as countermeasures against jamming attacks. Traditional anti-jamming techniques require that senders and receivers share a secret key in order to communicate with each other. However, such a requirement prevents these techniques from being effective for anti-jamming broadcast communication, where a jammer may learn the shared key from a compromised or malicious receiver and disrupt the reception at normal receivers. In this paper, we propose a Randomized Differential DSSS (RD-DSSS) scheme to achieve anti-jamming broadcast communication without shared keys. RD-DSSS encodes each bit of data using the correlation of unpredictable spreading codes. Specifically, bit ``0'' is encoded using two different spreading codes, which have low correlation with each other, while bit ``1'' is encoded using two identical spreading codes, which have high correlation. To defeat reactive jamming attacks, RD-DSSS uses multiple spreading code sequences to spread each message and rearranges the spread output before transmitting it. Our theoretical analysis and simulation results show that RD-DSSS can effectively defeat jamming attacks for anti-jamming broadcast communication without shared keys.

Spread spectrum techniques (e.g., Frequency Hopping (FH), Direct Sequence Spread Spectrum (DSSS)) have been widely used for anti-jamming wireless communications. Such techniques require that communicating devices agree on a shared secret before communication. However, it is non-trivial for two devices that do not share any secret to establish one in presence of a jammer. Recently, several schemes relying on Uncoordinated Frequency Hopping (UFH) were proposed to allow two devices to establish a secret key using Diffie-Hellman (DH) key establishment protocol in presence of jammers. Unfortunately, all these schemes are limited in efficiency. In this paper, we propose a novel scheme named USD-FH, which uses Uncoordinated Seed Disclosure in Frequency Hopping to establish a shared secret in presence of jammers. The basic idea is to transmit each DH key establishment message using a one-time pseudo-random hopping pattern and disclose the corresponding seed in an uncoordinated manner before the actual message. Due to the large number of channels available for wireless communication, the jammers cannot control all channels at the same time. When the receiver and the sender use the same channel during seed disclosure, the receiver can get the seed. If the jammer does not listen on the same channel (and thus it does not know the hopping pattern), the receiver can receive the actual message without being jammed. We validate USD-FH through both theoretical analysis and simulation. Our results show that USD-FH is much more efficient and robust than previous solutions.

To address the increasing demand for wireless bandwidth, cognitive radio networks (CRNs) have been proposed to increase the efficiency of channel utilization; they enable the sharing of channels among secondary (unlicensed) and primary (licensed) users on a non-interference basis. A secondary user in a CRN should constantly monitor for the presence of a primary user's signal to avoid interfering with the primary user. However, to gain unfair share of radio channels, an attacker (e.g., a selfish secondary user) may mimic a primary user's signal to evict other secondary users. Therefore, a secure primary user detection method that can distinguish a primary user's signal from an attacker's signal is needed. A unique challenge in addressing this problem is that Federal Communications Commission (FCC) prohibits any modification to primary users. Consequently, existing cryptographic techniques cannot be used directly. In this paper, we develop a novel approach for authenticating primary users' signals in CRNs, which conforms to FCC's requirement. Our approach integrates cryptographic signatures and wireless link signatures (derived from physical radio channel characteristics) to enable primary user detection in the presence of attackers. Essential to our approach is a {\em helper node} placed physically close to a primary user. The helper node serves as a "bridge" to enable a secondary user to verify cryptographic signatures carried by the helper node's signals and then obtain the helper node's authentic link signatures to verify the primary user's signals. A key contribution in our paper is a novel physical layer authentication technique that enables the helper node to authenticate signals from its associated primary user. Unlike previous techniques for link signatures, our approach explores the geographical proximity of the helper node to the primary user, and thus does not require any training process.

Integrity measurement is a key issue in building trust in distributed systems. A good solution to integrity measurement has to provide both strong isolation between the measurement agent and the measurement target and time of check to time of use (TOCTTOU) consistency (i.e., the consistency between measured version and executed version throughout the lifetime of the target). Unfortunately, none of the previous approaches provide (or can be easily modified to provide) both capabilities. This paper presents HIMA, a hypervisor-based agent that measures the integrity of virtual machines (VMs) running on top of the hypervisor, which provides both capabilities identified above. HIMA performs two complementary tasks: (1) active monitoring of critical guest events and (2) guest memory protection. The former guarantees that the integrity measures are refreshed whenever the guest VM memory layout changes (e.g., upon creation of processes), while the latter ensures that integrity measurement of user programs cannot be bypassed without HIMA's knowledge. This paper also reports the experimental evaluation of a HIMA prototype using both micro-benchmark and application benchmark; the experimental results indicate that HIMA is a practical solution for real world applications.

Audit logs, providing information about the current and past states of systems, are one of the most important parts of modern computer systems. Providing security for audit logs on an untrusted machine in a large distributed system is a challenging task, especially in the presence of active adversaries. In such a system, it is critical to have forward security such that when an adversary compromises a machine, she cannot modify or forge the log entries accumulated before the compromise. Unfortunately, existing secure audit logging schemes have significant limitations that make them impractical for real-life applications: existing public key cryptography (PKC) based schemes are computationally expensive for logging in task intensive or resource-constrained systems, while existing symmetric schemes are not publicly verifiable and incur significant storage and communication overheads. In this paper, we propose a novel forward secure and aggregate logging scheme called blind-aggregate-forward (BAF) logging scheme, which is suitable for large distributed systems. BAF can produce publicly verifiable forward secure and aggregate signatures with near-zero computational, storage, and communication costs for the loggers, without requiring any online trusted third party (TTP) support. We prove that BAF is secure under appropriate computational assumptions, and demonstrate that BAF is significantly more efficient and scalable than the previous schemes. Therefore, BAF is an ideal solution for secure logging in both task intensive and resource-constrained systems.

A power grid is a complex system connecting electric power generators to consumers through power transmission and distribution networks across a large geographical area. System monitoring is necessary to ensure the reliable operation of power grids, and state estimation is used in system monitoring to best estimate the power grid state through analysis of meter measurements and power system models. Various techniques have been developed to detect and identify bad measurements, including the interacting bad measurements introduced by arbitrary, non-random causes. At first glance, it seems that these techniques can also defeat malicious measurements injected by attackers.

Kernel rootkits have posed serious security threats due to their stealthy manner. To hide their presence and activities, many rootkits hijack control flows by modifying control data or hooks in the kernel space. A critical step towards eliminating rootkits is to protect such hooks from being hijacked. However, it remains a challenge because there exist a large number of widely-scattered kernel hooks and many of them could be dynamically allocated from kernel heap and co-located together with other kernel data. In addition, there is a lack of flexible commodity hardware support, leading to the socalled protection granularity gap -- kernel hook protection requires byte-level granularity but commodity hardware only provides page level protection.

Remote attestation of system integrity is an essential part of trusted computing. However, current remote attestation techniques only provide integrity proofs of static properties of the system. To address this problem we present a novel remote dynamic attestation system named ReDAS (Remote Dynamic Attestation System) that provides integrity evidence for dynamic system properties. Such dynamic system properties represent the runtime behavior of the attested system, and enable an attester to prove its runtime integrity to a remote party. ReDAS currently provides two types of dynamic system properties for running applications: structural integrity and global data integrity. In this work, we present the challenges of remote dynamic attestation, provide an in-depth security analysis and introduce a first step towards providing a complete runtime dynamic attestation framework. Our prototype implementation and evaluation with real-world applications show that we can improve on current static attestation techniques with an average performance overhead of 8%.

Wireless sensor networks are considered ideal candidates for a wide range of applications. It is desirable and sometimes necessary to reprogram sensor nodes through wireless links after they are deployed to remove bugs or add new functionalities. Several approaches (e.g., Seluge, Sluice) have been proposed recently for secure code dissemination in wireless sensor networks, all as security extensions to the state-of-the- art code dissemination system named Deluge. However, existing approaches all focused on securing the propagation of code images, but overlooked the security vulnerabilities in other image management aspects such as rebooting and erasing code images. In this paper, we identify the security vulnerabilities in epidemic image management in all existing solutions to secure code dissemination in wireless sensor networks. Such vulnerabilities allow an attacker to reboot a sensor network to undesirable images or erase critical images, exposing the network to security risks. We then develop a sequence of lightweight techniques to address these vulnerabilities. Our approach takes into consideration the limited resources on current sensor platforms, and removes the security vulnerabilities without introducing significant overhead. To evaluate the feasibility of our approach, we implement the proposed approach as a remote image management system named Seluge-ImageMan, which is intended to work with Seluge, a security extension to Deluge for injecting new code images. We perform a substantial set of experiments in the WiSeNeT sensor testbed, which consists of 72 MicaZ motes, to assess the performance overhead of Seluge-ImageMan. The experimental results indicate that our approach introduces very light overhead while completing the secure remote code image management solution for wireless sensor networks.

Unattended Wireless Sensor Networks (UWSNs) operating in hostile environments face great security and performance challenges due to the lack of continuous real-time communication between senders (sensors) and receivers (e.g., mobile data collectors, static sinks). The lack of real-time communication

In wireless sensor networks, it is critical to restrict the network access only to eligible sensor nodes, while messages from outsiders will not be forwarded in the networks. In this paper, we present the design, implementation, and evaluation of a secure network access system for wireless sensor networks. This paper makes three contributions: First, it develops a network admission control subsystem using Elliptic Curve public key cryptosystem to add new sensor nodes into a sensor network. The admission control subsystem employs a polynomial-based weak authentication scheme to mitigate Denial of Service (DoS) attacks against the public key cryptographic operations. Second, it implements an interface in TinyOS to provide symmetric key cryptography using the hardware security support in IEEE 802.15.4 radio components (e.g., CC2420). The hardware security can satisfy both message authentication and timely delivery requirements in real-time applications. The third contribution is an implementation of a stateless group key update scheme to update a network-wide secret key in a sensor network. We implement all the proposed techniques on Imote2 sensor platform running TinyOS and conduct an evaluation through field experiments.

Broadcast authentication is an important functionality in sensor networks. Energy constraints on sensor nodes and the real-time nature of the broadcasts render many of the existing solutions impractical: previous works focusing primarily on symmetric key schemes have difficulties in achieving real-time authentication. Public Key Cryptography (PKC), however, can satisfy the real-time requirements, and recent trends indicate that public key is becoming feasible for sensor networks. However, PKC operations are still expensive computations. It is impractical to use PKC in the conventional ways for broadcast authentication in sensor networks. To reduce costs, we propose ShortPK , an efficient Short-term Public Key broadcast authentication scheme. The basic idea is to use short-length public/private keys, but limit their lifetime to only a short period of time. To cover a long period of time, we need to use many public/private key pairs; distributing these public keys to sensors is a challenging problem. We describe a progressive key distribution scheme that is secure, efficient, and packet-loss resilient. We compare our scheme with the traditional 160-bit ECC public key schemes, and show that our scheme can achieve a significant improvement on energy consumption.

Wireless sensor networks are considered ideal candidates for a wide range of applications, such as industry monitoring, data acquisition in hazardous environments, and military operations. It is desirable and sometimes necessary to reprogram sensor nodes through wireless links after deployment, due to, for example, the need of removing bugs and adding new functionalities. The process of propagating a new code image to the nodes in a wireless sensor network is referred to as code dissemination. This paper presents the design, implementation, and evaluation of an efficient, secure, robust, and DoS-resistant code dissemination system named Seluge for wireless sensor networks. Seluge is a secure extension to Deluge, an open source, state-of-the-art code dissemination system for wireless sensor networks. It provides security protections for code dissemination, including the integrity protection of code images and immunity from, to the best of our knowledge, all DoS attacks that exploit code dissemination protocols. Seluge is superior to all previous attempts for secure code dissemination, and is the only solution that seamlessly integrates the security mechanisms and the Deluge efficient propagation strategies. Besides the theoretical analysis that demonstrates the security and performance of Seluge, this paper also reports the experimental evaluation of Seluge in a network of MicaZ motes, which shows the efficiency of Seluge in practice.

Public key cryptography (PKC) has been the enabling technology underlying many security services and protocols in traditional networks such as the Internet. In the context of wireless sensor networks, elliptic curve cryptography (ECC), one of the most efficient types of PKC, is being investigated to provide PKC support in sensor network applications so that the existing PKC-based solutions can be exploited. This paper presents the design, implementation, and evaluation of TinyECC, a configurable library for ECC operations in wireless sensor networks. The primary objective of TinyECC is to provide a ready-to-use, publicly available software package for ECC-based PKC operations that can be flexibly configured and integrated into sensor network applications. TinyECC provides a number of optimization switches, which can turn specific optimizations on or off based on developers' needs. Different combinations of the optimizations have different execution time and resource consumptions, giving developers great flexibility in integrating TinyECC into sensor network applications. This paper also reports the experimental evaluation of TinyECC on several common sensor platforms, including MICAz, Tmote Sky, and Imotel. The evaluation results show the impacts of individual optimizations on the execution time and resource consumptions, and give the most computationally efficient and the most storage efficient configuration of TinyECC.

A wireless sensor network is expected to consist of a potentially large number of low-cost, low-power, and multi-functional sensor nodes that communicate over short distances through wireless links. Due to their potential to provide fine-grained sensing and actuation at a reasonable cost, wireless sensor networks are considered ideal candidates for a wide range of applications, such as industry monitoring, data acquisition in hazardous environments, and military operations.

Recent studies have demonstrated that it is possible to perform public key cryptographic operations on the resource-constrained sensor platforms. However, the significant resource consumption imposed by public key cryptographic operations makes such mechanisms easy targets of Denial- of Service (DoS) attacks. For example, if digital signatures such as ECDSA are used directly for broadcast authentication without further protection, an attacker can simply broadcast forged packets and force the receiving nodes to perform a large number of unnecessary signature verifications, eventually exhausting their battery power. This paper studies how to deal with such DoS attacks when signatures are used for broadcast authentication in sensor networks. In particular, this paper presents two filtering techniques, a group-based filter and a key chain-based filter, to handle DoS attacks against signature verification. Both methods can significantly reduce the number of unnecessary signature verifications that a sensor node has to perform. The analytical results also show that these two techniques are efficient and effective for resource-constrained sensor networks.

Many sensor network applications require sensors' locations to function correctly. Despite the recent advances, location discovery for sensor networks in hostile environments has been mostly overlooked. Most of the existing localization protocols for sensor networks are vulnerable in hostile environments. The security of location discovery can certainly be enhanced by authentication. However, the possible node compromises and the fact that location determination uses certain physical features (e.g., received signal strength) of radio signals make authentication not as effective as in traditional security applications. This article presents two methods to tolerate malicious attacks against range-based location discovery in sensor networks. The first method filters out malicious beacon signals on the basis of the “consistency” among multiple beacon signals, while the second method tolerates malicious beacon signals by adopting an iteratively refined voting scheme. Both methods can survive malicious attacks even if the attacks bypass authentication, provided that the benign beacon signals constitute the majority of the beacon signals. This article also presents the implementation and experimental evaluation (through both field experiments and simulation) of all the secure and resilient location estimation schemes that can be used on the current generation of sensor platforms (e.g., MICA series of motes), including the techniques proposed in this article, in a network of MICAz motes. The experimental results demonstrate the effectiveness of the proposed methods, and also give the secure and resilient location estimation scheme most suitable for the current generation of sensor networks.

Sensor networks are often subject to physical attacks. Once a node's cryptographic key is compromised, an attacker may completely impersonate it and introduce arbitrary false information into the network. Basic cryptographic mechanisms are often not effective in this situation. Most techniques to address this problem focus on detecting and tolerating false information introduced by compromised nodes. They cannot pinpoint exactly where the false information is introduced and who is responsible for it. In this article, we propose an application-independent framework for accurately identifying compromised sensor nodes. The framework provides an appropriate abstraction of application-specific detection mechanisms and models the unique properties of sensor networks. Based on the framework, we develop alert reasoning algorithms to identify compromised nodes. The algorithm assumes that compromised nodes may collude at will. We show that our algorithm is optimal in the sense that it identifies the largest number of compromised nodes without introducing false positives. We evaluate the effectiveness of the designed algorithm through comprehensive experiments.

Many key predistribution techniques have been developed recently to establish pairwise keys between sensor nodes in wireless sensor networks. To further improve these schemes, researchers have also proposed to take advantage of the sensors' expected locations and discovered locations to help the predistribution of the keying materials. However, in many cases, it is very difficult to deploy sensor nodes at their expected locations or guarantee the correct location discovery at sensor nodes in hostile environments. In this article, a group-based deployment model is developed to improve key predistribution. In this model, sensor nodes are only required to be deployed in groups. The critical observation in the article is that the sensor nodes in the same group are usually close to each other after deployment . This deployment model is practical; it greatly simplifies the deployment of sensor nodes, while still providing an opportunity to improve key predistribution. Specifically, the article presents a novel framework for improving key predistribution using the group-based deployment knowledge. This framework does not require the knowledge of the sensors' expected or discovered locations and is thus suitable for applications where it is difficult to deploy the sensor nodes at their expected locations or correctly estimate the sensors' locations after deployment. To seek practical key predistribution schemes, the article presents two efficient instantiations of this framework, a hash key-based scheme and a polynomial-based scheme . The evaluation shows that these two schemes are efficient and effective for pairwise key establishment in sensor networks; they can achieve much better performance than the previous key predistribution schemes when the sensor nodes are deployed in groups.

Broadcast authentication is a critical security service in wireless sensor networks. There are two general approaches for broadcast authentication in wireless sensor networks: digital signatures and μTESLA-based techniques. However, both signature-based and μTESLA-based broadcast authentication are vulnerable to Denial of Services (DoS) attacks: An attacker can inject bogus broadcast packets to force sensor nodes to perform expensive signature verifications (in case of signature-based broadcast authentication) or packet forwarding (in case of μTESLA-based broadcast authentication), thus exhausting their limited battery power. This paper presents an efficient mechanism called message-specific puzzle to mitigate such DoS attacks. In addition to signature-based or μTESLA-based broadcast authentication, this approach adds a weak authenticator in each broadcast packet, which can be efficiently verified by a regular sensor node, but takes a computationally powerful attacker a substantial amount of time to forge. Upon receiving a broadcast packet, each sensor node first verifies the weak authenticator, and performs the expensive signature verification (in signature-based broadcast authentication) or packet forwarding (in μTESLA-based broadcast authentication) only when the weak authenticator is valid. A weak authenticator cannot be precomputed without a non-reusable (or short-lived) key disclosed only in a valid packet. Even if an attacker has intensive computational resources to forge one or more weak authenticators, it is difficult to reuse these forged weak authenticators. Thus, this weak authentication mechanism substantially increases the difficulty of launching successful DoS attacks against signature-based or μTESLA-based broadcast authentication. A limitation of this approach is that it requires a powerful sender and introduces sender-side delay. This article also reports an implementation of the proposed techniques on TinyOS, as well as initial experimental evaluation in a network of MICAz motes.

Understanding security bugs in a vulnerable program is a non-trivial task, even if the target program is known to be vulnerable. Though there exist debugging tools that facilitate the vulnerability analysis and debugging process, human developers still need to manually trace the program execution most of the times. This makes security debugging a difficult and tiresome task even for experienced programmers. In this paper, we present the development of a novel security debugging tool called CBones (SeeBones, where bones is an analogy of program structures). CBones is intended to fully automate the analysis of a class of security vulnerabilities in C programs, the exploits of which would compromise the integrity of program structures satisfied by all legitimate binaries compiled from C source code. In other words, CBones automatically discovers how unknown vulnerabilities in C programs are exploited based on program structural constraints. Unlike the previous approaches, CBones can automatically identify exploit points of unknown security bugs without requiring a training phase, source code access (analysis or instrumentation), or additional hardware supports. To validate the effectiveness of this approach, we evaluate CBones with 12 real-world applications that contain a wide range of vulnerabilities. Our results show that CBones can discover all security bugs with no false alarms, pinpoint the corrupting instructions, and provide information to facilitate the understanding of how an attack exploits a security bug.

Software vulnerabilities have been the main contributing factor to the Internet security problems such as fast spreading worms. Among these software vulnerabilities, memory corruption vulnerabilities such as buffer overflow and format string bugs have been the most common ones exploited by network-based attacks. Many security countermeasures (e.g., patching, automatic signature generation for intrusion detection systems) require vulnerability information to function correctly. However, despite many years of research, automatically identifying unknown software vulnerabilities still remains an open problem.In this paper, we present the development of a security debugging tool named MemSherlock, which can automatically identify unknown memory corruption vulnerabilities upon the detection of malicious payloads that exploit such vulnerabilities. MemSherlock provides critical information for unknown memory corruption vulnerabilities, including (1) the corruption point in the source code (i.e., the statement that allows the exploitation of memory corruption vulnerability), (2) the slice of source code that helps the malicious input to reach the corruption point, and (3) the description of how the malicious input exploits the unknown vulnerability. We evaluate MemSherlock with a set of 11 real-world applications that have buffer overflow, heap overflow, and format string vulnerabilities. The evaluation results indicate that MemSherlock is a useful tool to facilitate the automatic vulnerability analysis process.

Anonymity is increasingly important for network applications concerning about censorship and privacy. The existing anonymous communication protocols generally stem from mixnet and DC-net. They either cannot provide provable anonymity or suffer from transmission collision. In this paper, we introduce a novel approach which takes advantage of hierarchical ring structure and mix technique. This proposed protocol is collision free and provides provable k-anonymity for both the sender and the recipient, even if a polynomial time adversary can eavesdrop all network traffic and control a fraction of participants. Furthermore, it can hide the sender and the recipient from each other and thus can be used for anonymous file sharing. The analysis shows the proposed protocol is secure against various attacks. Measurements further demonstrate it is practical.

Remotely-launched software exploits are a common way for attackers to intrude into vulnerable computer systems. As detection techniques improve, remote exploitation techniques are also evolving. Recent techniques for evasion of exploit detection include polymorphism (code encryption) and meta-morphism (code obfuscation). This paper addresses the problem of detecting in network traffic polymorphic remote exploits that are encrypted, and that self-decrypt before launching the intrusion. Such exploits pose a great challenge to existing malware detection techniques, partly due to the non-obvious starting location of the exploit code in the network payload.We describe a new method for detecting self-decrypting exploit codes. This method scans network traffic for the presence of a decryption routine, which is characteristic of such exploits. The proposed method uses static analysis and emulated instruction execution techniques. This improves the accuracy of determining the starting location and instructions of the decryption routine, even if self-modifying code is used. The method outperforms approaches that have been previously proposed, both in terms of detection capabilities, and in detection accuracy.The proposed method has been implemented and tested on current polymorphic exploits, including ones generated by state-of-the-art polymorphic engines. All exploits have been detected (i.e., a 100% detection rate), including those for which the decryption routine is dynamically coded, or self-modifying. The false positive rate is close to 0%. Running time is approximately linear in the size of the network payload being analyzed.

Tracing interactive traffic that traverses stepping stones (i.e., intermediate hosts) is challenging, as the packet headers, lengths, and contents can all be changed by the stepping stones. The traffic timing has therefore been studied as a means of tracing traffic. One such technique uses traffic timing as a side channel into which a watermark, or identifying tag, can be embedded to aid with tracing. The effectiveness of such techniques is greatly reduced when repacketization of the traffic occurs at the stepping stones. Repacketization is a natural effect of many applications, including SSH, and therefore poses a serious challenge for traffic tracing. This paper presents a new method of embedding a watermark in traffic timing, for purposes of tracing the traffic in the presence of repacketization. This method uses an invariant characteristic of two traffic flows which are part of the same stepping stone chain, namely, elapsed time of the flows. The duration of each flow is sliced into short fixed-length intervals. Packet timing is adjusted to manipulate the packet count in specific intervals, for purposes of embedding the watermark. A statistical analysis of the method, with no assumptions or limitations concerning the distribution of packet times, proves the effectiveness of the method given a sufficient number of packets, despite natural and/or deliberate repacketization and perturbation of the traffic timing by an adversary. The method has been implemented and tested on a large number of synthetically-generated SSH traffic flows. The results demonstrate that 100% detection rates and less than 1% false positive rates are achievable under conditions of 2 seconds of maximum timing perturbation and 12% repacketization rate, using fewer than 1000 packets.

Broadcast authentication is an important application in sensor networks. Public Key Cryptography (PKC) is desirable for this application, but due to the resource constraints on sensor nodes, these operations are expensive, which means sensor networks using PKC are susceptible to Denial of Service (DoS) attacks: attackers keep broadcasting bogus messages, which will incur extra costs, thus exhaust the energy of the honest nodes. In addition, the long time to verify each message using PKC increases the response time of the nodes; it is impractical for the nodes to validate each incoming message before forwarding i.

Vehicular ad hoc networks (VANETs) are being advocated for traffic control, accident avoidance, and a variety of other applications. Security is an important concern in VANETs because a malicious user may deliberately mislead other vehicles and vehicular agencies. One type of malicious behavior is called a Sybil attack, wherein a malicious vehicle pretends to be multiple other vehicles. Reported data from a Sybil attacker will appear to arrive from a large number of distinct vehicles, and hence will be credible. This paper proposes a light-weight and scalable framework to detect Sybil attacks. Importantly, the proposed scheme does not require any vehicle in the network to disclose its identity, hence privacy is preserved at all times. Simulation results demonstrate the efficacy of our protocol.

Sensor networks are often deployed in unattended environments, thus leaving these networks vulnerable to false data injection attacks in which an adversary injects false data into the network with the goal of deceiving the base station or depleting the resources of the relaying nodes. Standard authentication mechanisms cannot prevent this attack if the adversary has compromised one or a small number of sensor nodes. We present three interleaved hop-by-hop authentication schemes that guarantee that the base station can detect injected false data immediately when no more than t nodes are compromised, where t is a system design parameter. Moreover, these schemes enable an intermediate forwarding node to detect and discard false data packets as early as possible. Our performance analysis shows that our scheme is efficient with respect to the security it provides, and it also allows a tradeoff between security and performance. A prototype implementation of our scheme indicates that our scheme is practical and can be deployed on the current generation of sensor nodes.

Address space randomization is an emerging and promising method for stopping a broad range of memory corruption attacks. By randomly shifting critical memory regions at process initialization time, address space randomization converts an otherwise successful malicious attack into a benign process crash. However, existing approaches either introduce insufficient randomness, or require source code modification. While insufficient randomness allows successful brute-force attacks, as shown in recent studies, the required source code modification prevents this effective method from being used for commodity software, which is the major source of exploited vulnerabilities on the Internet. We propose Address Space Layout Permutation (ASLP) that introduces high degree of randomness (or high entropy) with minimal performance overhead. Essential to ASLP is a novel binary rewriting tool that can place the static code and data segments of a compiled executable to a randomly specified location and performs fine-grained permutation of procedure bodies in the code segment as well as static data objects in the data segment. We have also modified the Linux operating system kernel to permute stack, heap, and memory mapped regions. Together, ASLP completely permutes memory regions in an application. Our security and performance evaluation shows minimal performance overhead with orders of magnitude improvement in randomness (e.g., up to 29 bits of randomness on a 32-bit architecture).

In wireless sensor networks, clustering sensor nodes into small groups is an effective technique to achieve scalability, self-organization, power saving, channel access, routing, etc. A number of cluster formation protocols have been proposed recently. However, most existing protocols assume benign environments, and are vulnerable to attacks from malicious nodes. In this paper, we propose a secure distributed cluster formation protocol to organize sensor networks into mutually disjoint cliques. Our protocol has the following properties: (1) normal nodes are divided into mutually disjoint cliques; (2) all the normal nodes in each clique agree on the same clique memberships; (3) while external attackers can be prevented from participating in the cluster formation process, inside attackers that do not follow the protocol semantics can be identified and removed from the network; (4) the communication overhead is moderate; (5) the protocol is fully distributed.

Accurate and synchronized time is crucial in many sensor network applications due to the need for consistent distributed sensing and coordination. In hostile environments where an adversary may attack the networks and/or the applications through external or compromised nodes, time synchronization becomes an attractive target due to its importance. This paper describes the design, implementation, and evaluation of TinySeRSync, a secure and resilient time synchronization subsystem for wireless sensor networks running TinyOS. This paper makes three contributions: First, it develops a secure single-hop pairwise time synchronization technique using hardware-assisted, authenticated medium access control (MAC) layer timestamping. Unlike the previous attempts, this technique can handle high data rate such as those produced by MICAz motes (in contrast to those by MICA2 motes). Second, this paper develops a secure and resilient global time synchronization protocol based on a novel use of the μTESLA broadcast authentication protocol for local authenticated broadcast, resolving the conflict between the goal of achieving time synchronization with μTESLA-based broadcast authentication and the fact that μTESLA requires loose time synchronization. The resulting protocol is secure against external attacks and resilient against compromised nodes. The third contribution consists of an implementation of the proposed techniques on MICAz motes running TinyOS and a thorough evaluation through field experiments in a network of 60 MICAz motes.

Timing-based active watermarking schemes are developed to trace back attackers through stepping stone connections or anonymizing networks. By slightly changing packet timing, these schemes achieve robust correlation for encrypted network connections under timing perturbation. However, the manipulation on packet timing makes the schemes themselves a potential target of intelligent attackers. In this paper, we analyze the secrecy of the timing-based active watermarking techniques for tracing through stepping stones, and propose an attack scheme based on analyzing the packet delays between adjacent stepping stones. We develop attack techniques to infer important watermark parameters, and to recover and duplicate embedded watermarks. The resulting techniques enable an attacker to defeat the tracing systems in certain cases by removing watermarks from the stepping stone connections, or replicating watermarks in non-stepping stone connections. We also develop techniques to determine in real-time whether a stepping stone connection is being watermarked for trace-back purposes. We have performed substantial experiments using real-world data to evaluate these techniques. The experimental results demonstrate that for the watermark scheme being attacked (1) embedded watermarks can be successfully recovered and duplicated when the watermark parameters are not chosen carefully, and (2) the existence of watermarks in a network flow can always be quickly detected.

Wireless sensor networks have received a lot of attention recently due to its wide applications. An accurate and synchronized clock time is crucial in many sensor network applications. Several clock synchronization schemes have been proposed for wireless sensor networks recently to address the resource constraints in such networks. However, most of these techniques assume benign environments, but cannot survive malicious attacks in hostile environments, especially when there are compromised nodes. As an exception, a recent work attempts to detect malicious attacks against clock synchronization, and aborts when an attack is detected. Though this approach can prevent incorrect clock synchronization due to attacks, it will lead to denial of clock synchronization in such situations. This paper adopts a model where all the sensor nodes synchronize their clocks to a common source, which is assumed to be well synchronized to the external clock. This paper seeks techniques to provide redundant ways for each node to synchronize its clock with the common source, so that it can tolerate partially missing or false synchronization information provided by compromised nodes. Two types of techniques are developed using this general method: level-based clock synchronization and diffusion-based clock synchronization. Targeted at static sensor networks, the level-based clock synchronization constructs a level hierarchy initially, and uses (or reuses) this level hierarchy for multiple rounds of clock synchronization. The diffusion-based clock synchronization attempts to synchronize all the clocks without relying on any structure assumptions and, thus, can be used for dynamic sensor networks. This paper further investigates how to use multiple clock sources for both approaches to increase the resilience against compromise of source nodes. The analysis in this paper indicates that both level-based and diffusion-based approaches can tolerate up to s colluding malicious source nodes and t colluding malicious nodes among the neighbors of each normal node, where s and t are two system parameters. This paper also presents the results of simulation studies performed to evaluate the proposed techniques. These results demonstrate that the level-based approach has less overhead and higher precision, but less coverage, than the diffusion-based approach.

With the increasing security threats from infrastructure attacks such as worms and distributed denial of service attacks, it is clear that the cooperation among different organizations is necessary to defend against these attacks. However, organizations' privacy concerns for the incident and security alert data require that sensitive data be sanitized before they are shared with other organizations. Such sanitization process usually has negative impacts on intrusion analysis (such as alert correlation). To balance the privacy requirements and the need for intrusion analysis, we propose a privacy-preserving alert correlation approach based on concept hierarchies. Our approach consists of two phases. The first phase is entropy guided alert sanitization, where sensitive alert attributes are generalized to high-level concepts to introduce uncertainty into the dataset with partial semantics. To balance the privacy and the usability of alert data, we propose to guide the alert sanitization process with the entropy or differential entropy of sanitized attributes. The second phase is sanitized alert correlation. We focus on defining similarity functions between sanitized attributes and building attack scenarios from sanitized alerts. Our preliminary experimental results demonstrate the effectiveness of the proposed techniques.

Cyber attacks against networked computers have become relentless in recent years. The most common attack method is to exploit memory corruption vulnerabilities such as buffer overflow and format string bugs. This paper presents a technique to automatically identify both known and unknown memory corruption vulnerabilities. Based on the observation that a randomized program usually crashes upon a memory corruption attack, this technique uses the crash as a trigger to initiate an automatic diagnosis algorithm. The output of the diagnosis includes the instruction that is tricked to corrupt data, the call stack at the time of corruption, and the propagation history of corrupted data. These results provide useful information in fixing the vulnerabilities. Moreover, the diagnosis process also generates a signature of the attack using data/address values embedded in the malicious input message, and is used to block future attacks. Such a signature is further associated with the program execution state to reduce false positives without decreasing the detection rate. The proposed techniques enable the development of a decentralized self-diagnosing and self-protecting defense mechanism for networked computers. We report the implementation experience and experimental evaluation of a prototype system on Linux.

Sensors' locations play a critical role in many sensor network applications. A number of techniques have been proposed recently to discover the locations of regular sensors based on a few special nodes called beacon nodes, which are assumed to know their locations (e.g., through GPS receivers or manual configuration). However, none of these techniques can work properly when there are malicious attacks, especially when some of the beacon nodes are compromised. This paper introduces a suite of techniques to detect and remove compromised beacon nodes that supply misleading location information to the regular sensors, aiming at providing secure location discovery services in wireless sensor networks. These techniques start with a simple but effective method to detect malicious beacon signals. To identify malicious beacon nodes and avoid false detection, this paper also presents several techniques to detect replayed beacon signals. This paper then proposes a method to reason about the suspiciousness of each beacon node at the base station based on the detection results collected from beacon nodes, and then revoke malicious beacon nodes accordingly. Finally, this paper provides detailed analysis and simulation to evaluate the proposed techniques. The results show that our techniques are practical and effective in detecting malicious beacon nodes

In wireless sensor networks (WSNs), sensor location plays a critical role in many applications. Having a GPS receiver on every sensor node is costly. In the past, a number of location discovery schemes have been proposed. Most of these schemes share a common feature: they use some special nodes, called beacon nodes, which are assumed to know their own locations (e.g., through GPS receivers or manual configuration). Other sensors discover their locations based on the information provided by these beacon nodes. In this paper, we show that efficient location discovery can be achieved in sensor networks without using beacons. We propose a beacon-less location discovery scheme, based on the following observations: in practice, it is quite common that sensors are deployed in groups, i.e., sensors are put into n groups, and sensors in the same group are deployed together at the same deployment point (the deployment point is different from the sensors' final resident location). Sensors from the same group can land in different locations, and those locations usually follow a probability distribution that can be known a priori. With this prior deployment knowledge, we show that sensors can discover their locations by observing the group memberships of its neighbors. We model the location discovery problem as a statistical estimation problem, and we use the maximum likelihood estimation method to estimate the location. We have conducted experiments to evaluate our scheme.

Many sensor network applications require sensors' locations to function correctly. Despite the recent advances, location discovery for sensor networks in hostile environments has been mostly overlooked. Most of the existing localization protocols for sensor networks are vulnerable in hostile environments. The security of location discovery can certainly be enhanced by authentication. However, the possible node compromises and the fact that location determination uses certain physical features (e.g., received signal strength) of radio signals make authentication not as effective as in traditional security applications. This paper presents two methods to tolerate malicious attacks against beacon-based location discovery in sensor networks. The first method filters out malicious beacon signals on the basis of the "consistency" among multiple beacon signals, while the second method tolerates malicious beacon signals by adopting an it era lively refined voting scheme. Both methods can survive malicious attacks even if the attacks bypass authentication, provided that the benign beacon signals constitute the majority of the "consistent" beacon signals. This paper also presents the implementation of these techniques on MICA2 motes running TinyOS, and the evaluation through both simulation and field experiments. The experimental results demonstrate that the proposed methods are promising for the current generation of sensor networks.

With the advance of technology, Public Key Cryptography (PKC) will sooner or later be widely used in wireless sensor networks. Recently, it has been shown that the performance of some public-key algorithms, such as Elliptic Curve Cryptography (ECC), is already close to being practical on sensor nodes. However, the energy consumption of PKC is still expensive, especially compared to symmetric-key algorithms. To maximize the lifetime of batteries, we should minimize the use of PKC whenever possible in sensor networks.This paper investigates how to replace one of the important PKC operations--the public key authentication--with symmetric key operations that are much more efficient. Public key authentication is to verify the authenticity of another party's public key to make sure that the public key is really owned by the person it is claimed to belong to. In PKC, this operation involves an expensive signature verification on a certificate. We propose an efficient alternative that uses one-way hash function only. Our scheme uses all sensor's public keys to construct a forest of Merkle trees of different heights. By optimally selecting the height of each tree, we can minimize the computation and communication costs. The performance of our scheme is evaluated in the paper.

Broadcast authentication is a critical security service in sensor networks; it allows a sender to broadcast messages to multiple nodes in an authenticated way. /spl mu/TESLA and multi-level /spl mu/TESLA have been proposed to provide such services for sensor networks. However, none of these techniques are scalable in terms of the number of senders. Though multi-level /spl mu/TESLA schemes can scale up to large sensor networks (in terms of receivers), they either use substantial bandwidth and storage at sensor nodes, or require significant resources at senders to deal with DOS attacks. This paper presents efficient techniques to support a potentially large number of broadcast senders using /spl mu/TESLA instances as building blocks. The proposed techniques are immune to the DOS attacks. This paper also provides two approaches, a revocation tree based scheme and a proactive distribution based scheme, to revoke the broadcast authentication capability from compromised senders. The proposed techniques are implemented, and evaluated through simulation on TinyOS. The analysis and experiment show that these techniques are efficient and practical, and can achieve better performance than the previous approaches.

Auto-configuration provides convenience in implementing a mobile ad hoc network. However, it may sacrifice network security if there is not a reliable authentication mechanism, e.g., an attacker may spoof other nodes and hijack their traffic. In this paper, we propose a secure address auto-configuration scheme for MANET. It binds each IP address with a public key, allows a node to self-authenticate itself and thus thwarts address spoofing and other attacks associated with auto-configuration.

Wireless sensor networks have received a lot of attention recently due to their wide applications, such as target tracking, environment monitoring, and scientific exploration in dangerous environments. It is usually necessary to have a cluster of sensor nodes share a common view of a local clock time, so that all these nodes can coordinate in some important applications, such as time slotted MAC protocols, power-saving protocols with sleep/listen modes, etc. However, all the clock synchronization techniques proposed for sensor networks assume benign environments; they cannot survive malicious attacks in hostile environments. Fault-tolerant clock synchronization techniques are potential candidates to address this problem. However, existing approaches are all resource consuming and suffer from message collisions in most of cases. This paper presents a novel fault-tolerant clock synchronization scheme for clusters of nodes in sensor networks, where the nodes in each cluster can communicate through broadcast. The proposed scheme guarantees an upper bound of clock difference between any nonfaulty nodes in a cluster, provided that the malicious nodes are no more than one third of the cluster. Unlike the traditional fault-tolerant clock synchronization approaches, the proposed technique does not introduce collisions between synchronization messages, nor does it require costly digital signatures.

Pairwise key establishment is a fundamental security service in sensor networks; it enables sensor nodes to communicate securely with each other using cryptographic techniques. However, due to the resource constraints on sensor nodes, it is not feasible to use traditional key management techniques such as public key cryptography and key distribution center (KDC). A number of key predistribution techniques have been proposed for pairwise key establishment in sensor networks recently. To facilitate the study of novel pairwise key predistribution techniques, this paper develops a general framework for establishing pairwise keys between sensor nodes using bivariate polynomials. This paper then proposes two efficient instantiations of the general framework: a random subset assignment key predistribution scheme, and a hypercube-based key predistribution scheme. The analysis shows that both schemes have a number of nice properties, including high probability, or guarantee to establish pairwise keys, tolerance of node captures, and low storage, communication, and computation overhead. To further reduce the computation at sensor nodes, this paper presents an optimization technique for polynomial evaluation, which is used to compute pairwise keys. This paper also reports the implementation and the performance of the proposed schemes on MICA2 motes running TinyOS, an operating system for networked sensors. The results indicate that the proposed techniques can be applied efficiently in resource-constrained sensor networks.

Pairwise key establishment is a fundamental security service for sensor networks. However, establishing pairwise keys in sensor networks is a challenging problem, particularly due to the resource constraints on sensor nodes and the threat of node compromises. This article proposes to use both predeployment and postdeployment knowledge to improve pairwise key predistribution in static sensor networks. By exploiting the predeployment knowledge, this article first develops two key predistribution schemes, a closest pairwise keys scheme and a closest polynomials scheme . The analysis shows that these schemes can achieve better performance if the expected location information is available and that the smaller the deployment error is, the better performance they can achieve. The article then investigates how to use postdeployment knowledge to improve pairwise key predistribution in static sensor networks. The idea is to load an excessive amount of predistributed keys on sensor nodes, prioritize these keys based on sensors' actual locations discovered after deployment, and discard low-priority keys to thwart node compromise attacks. This approach is then used to improve the random subset assignment scheme proposed recently to demonstrate its practicality and effectiveness. The analysis indicates that the postdeployment knowledge can also greatly improve the performance and security of key predistribution.

Complementary security systems are widely deployed in networks to protect digital assets. Alert correlation is essential to understanding the security threats and taking appropriate actions. This paper proposes a novel correlation approach based on triggering events and common resources. One of the key concepts in our approach is triggering events, which are the (low-level) events that trigger alerts. By grouping alerts that share "similar" triggering events, a set of alerts can be partitioned into different clusters such that the alerts in the same cluster may correspond to the same attack. Our approach further examines whether the alerts in each cluster are consistent with relevant network and host configurations, which help analysts to partially identify the severity of alerts and clusters. The other key concept in our approach is input and output resources. Intuitively, input resources are the necessary resources for an attack to succeed, and output resources are the resources that an attack supplies if successful. This paper proposes to model each attack through specifying input and output resources. By identifying the "common" resources between output resources of one attack and input resources of another, it discovers causal relationships between alert clusters and builds attack scenarios. The experimental results demonstrate the usefulness of the proposed techniques.

This paper presents techniques to integrate and reason about complementary intrusion evidence such as alerts generated by intrusion detection systems (IDSs) and reports by system monitoring or vulnerability scanning tools. To facilitate the modeling of intrusion evidence, this paper classifies intrusion evidence into either event-based evidence or state-based evidence. Event-based evidence refers to observations (or detections) of intrusive actions (e.g., IDS alerts), while state-based evidence refers to observations of the effects of intrusions on system states. Based on the interdependency between event-based and state-based evidence, this paper develops techniques to automatically integrate complementary evidence into Bayesian networks, and reason about uncertain or unknown intrusion evidence based on verified evidence. The experimental results in this paper demonstrate the potential of the proposed techniques. In particular, additional observations by system monitoring or vulnerability scanning tools can potentially reduce the false alert rate and increase the confidence in alerts corresponding to successful attacks.

Sensor networks are often deployed in unattended environments, thus leaving these networks vulnerable to false data injection attacks in which an adversary injects false data into the network with the goal of deceiving the base station or depleting the resources of the relaying nodes. Standard authentication mechanisms cannot prevent this attack if the adversary has compromised one or a small number of sensor nodes. In this paper, we present an interleaved hop-by-hop authentication scheme that guarantees that the base station will detect any injected false data packets when no more than a certain number t nodes are compromised. Further, our scheme provides an upper bound B for the number of hops that a false data packet could be forwarded before it is detected and dropped, given that there are up to t colluding compromised nodes. We show that in the worst case B is O(t/sup 2/). Through performance analysis, we show that our scheme is efficient with respect to the security it provides, and it also allows a tradeoff between security and performance.

Broadcast authentication is a fundamental security service in distributed sensor networks. This paper presents the development of a scalable broadcast authentication scheme named multilevel μTESLA based on μTESLA, a broadcast authentication protocol whose scalability is limited by its unicast-based initial parameter distribution. Multilevel μTESLA satisfies several nice properties, including low overhead, tolerance of message loss, scalability to large networks, and resistance to replay attacks as well as denial-of-service attacks. This paper also presents the experimental results obtained through simulation, which demonstrate the performance of the proposed scheme under severe denial-of-service attacks and poor channel quality.

Traditional intrusion detection systems (IDSs) focus on low-level attacks or anomalies, and raise alerts independently, though there may be logical connections between them. In situations where there are intensive attacks, not only will actual alerts be mixed with false alerts, but the amount of alerts will also become unmanageable. As a result, it is difficult for human users or intrusion response systems to understand the alerts and take appropriate actions. This paper presents a sequence of techniques to address this issue. The first technique constructs attack scenarios by correlating alerts on the basis of prerequisites and consequences of attacks. Intuitively, the prerequisite of an attack is the necessary condition for the attack to be successful, while the consequence of an attack is the possible outcome of the attack. Based on the prerequisites and consequences of different types of attacks, the proposed method correlates alerts by (partially) matching the consequences of some prior alerts with the prerequisites of some later ones. Moreover, to handle large collections of alerts, this paper presents a set of interactive analysis utilities aimed at facilitating the investigation of large sets of intrusion alerts. This paper also presents the development of a toolkit named TIAA, which provides system support for interactive intrusion analysis. This paper finally reports the experiments conducted to validate the proposed techniques with the 2000 DARPA intrusion detection scenario-specific datasets, and the data collected at the DEFCON 8 Capture the Flag event.

Several alert correlation methods have been proposed over the past several years to construct high-level attack scenarios from low-level intrusion alerts reported by intrusion detection systems (IDSs). However, all of these methods depend heavily on the underlying IDSs, and cannot deal with attacks missed by IDSs. In order to improve the performance of intrusion alert correlation and reduce the impact of missed attacks, this paper presents a series of techniques to hypothesize and reason about attacks possibly missed by the IDSs. In addition, this paper also discusses techniques to infer attribute values for hypothesized attacks, to validate hypothesized attacks through raw audit data, and to consolidate hypothesized attacks to generate concise attack scenarios. The experimental results in this paper demonstrate the potential of these techniques in building high-level attack scenarios.

Pairwise key establishment is a fundamental security service in sensor networks; it enables sensor nodes to communicate securely with each other using cryptographic techniques. However, due to the resource constraints on sensors, it is infeasible to use traditional key management techniques such as public key cryptography and key distribution center (KDC). To facilitate the study of novel pairwise key predistribution techniques, this paper presents a general framework for establishing pairwise keys between sensors on the basis of a polynomial-based key predistribution protocol [2]. This paper then presents two efficient instantiations of the general framework: a random subset assignment key predistribution scheme and a grid-based key predistribution scheme. The analysis in this paper indicates that these two schemes have a number of nice properties, including high probability (or guarantee) to establish pairwise keys, tolerance of node captures, and low communication overhead. Finally, this paper presents a technique to reduce the computation at sensors required by these schemes.

This paper presents group key distribution techniques for large and dynamic groups over unreliable channels. The techniques proposed here are based on the self-healing key distribution methods (with revocation capability) recently developed by Staddon et al. [31]. By introducing a novel personal key distribution technique, this paper reduces (1) the communication overhead of personal key share distribution from O(t log q) to O(t log q), (2) the communication overhead of self-healing key distribution with t-revocation capability from O((mt + tm) log q) to O(mt log q), and (3) the storage overhead of the self-healing key distribution with t-revocation capability at each group member from O(m log q) to O(m log q), where t is the maximum number of colluding group members, m is the number of sessions, and q is a prime number that is large enough to accommodate a cryptographic key. All these results are achieved without sacrificing the unconditional security of key distribution. In addition, this paper presents two techniques that allow trade-off between the broadcast size and the recoverability of lost session keys. These two methods further reduce the broadcast message size in situations where there are frequent but short-term disruptions of communication and where there are long-term but infrequent disruptions of communication, respectively. Finally, this paper presents an API implementation of the proposed techniques.

Understanding strategies of attacks is crucial for security applications such as computer and network forensics, intrusion response, and prevention of future attacks. This paper presents techniques to automatically learn attack strategies from correlated intrusion alerts. Central to these techniques is a model that represents an attack strategy as a graph of attacks with constraints on the attack attributes and the temporal order among these attacks. To learn the intrusion strategy is then to extract such a graph from a sequences of intrusion alerts. To further facilitate the analysis of attack strategies, which is essential to many security applications such as computer and network forensics, this paper presents techniques to measure the similarity between attack strategies. The basic idea is to reduces the similarity measurement of attack strategies into error-tolerant graph/subgraph isomorphism problem, and measures the similarity between attack strategies in terms of the cost to transform one strategy into another. Finally, this paper presents some experimental results, which demonstrate the potential of the proposed techniques.

Traditional intrusion detection systems (IDSs) focus on low-level attacks or anomalies, and raise alerts independently, though there may be logical connections between them. In situations where there are intensive attacks, not only will actual alerts be mixed with false alerts, but the amount of alerts will also become unmanageable. As a result, it is difficult for human users or intrusion response systems to understand the alerts and take appropriate actions. This paper presents a practical technique to address this issue. The proposed approach constructs attack scenarios by correlating alerts on the basis of prerequisites and consequences of attacks. Intuitively, the prerequisite of an attack is the necessary condition for the attack to be successful, while the consequence of an attack is the possible outcome of the attack. Based on the prerequisites and consequences of different types of attacks, our method correlates alerts by (partially) matching the consequences of some prior alerts with the prerequisites of some later ones. Moreover, to handle large collections of alerts, this paper presents three interactive analysis utilities aimed at reducing the complexity of the constructed attack scenarios without losing the structure of the attacks. This paper also reports the experiments conducted to validate the proposed techniques

A temporal association rule is an association rule that holds during specific time intervals. An example is that eggs and coffee are frequently sold together in morning hours. The paper studies temporal association rules during the time intervals specified by user-given calendar schemas. Generally, the use of calendar schemas makes the discovered temporal association rules easier to understand. An example of calendar schema is (year, month, day), which yields a set of calendar-based patterns of the form (d/sub 3/, d/sub 2/, d/sub 1/), where each d/sub i/ is either an integer or the symbol *. For example, (2000, *, 16) is such a pattern, which corresponds to the time intervals, each consisting of the 16th day of a month in year 2000. This paper defines two types of temporal association rules: precise-match association rules require that the association rule holds during every interval, and fuzzy-match ones require that the association rule holds during most of these intervals. The paper extends the well-known a priori algorithm, and also develops two optimization techniques to take advantage of the special properties of the calendar-based patterns. The experiments show that the algorithms and optimization techniques are effective.

Abstraction is an important issue in intrusion detection, since it not only hides the difference between heterogeneous systems, but also allows generic intrusion-detection models. However, abstraction is an error-prone process and is not well supported in current intrusion-detection systems (IDSs). This article presents a hierarchical model to support attack specification and event abstraction in distributed intrusion detection. The model involves three concepts: system view , signature , and view definition . A system view provides an abstract interface of a particular type of information; defined on the instances of system views, a signature specifies certain distributed attacks or events to be monitored; a view definition is then used to derive information from the matches of a signature and presents it through a system view. With the three elements, the model provides a hierarchical framework for maintaining signatures, system views, as well as event abstraction. As a benefit, the model allows generic signatures that can accommodate unknown variants of known attacks. Moreover, abstraction represented by a system view can be updated without changing either its specification or the signatures specified on its basis. This article then presents a decentralized method for autonomous but cooperative component systems to detect distributed attacks specified by signatures. Specifically, a signature is decomposed into finer units, called detection tasks , each of which represents the activity to be monitored on a component system. The component systems (involved in a signature) then perform the detection tasks cooperatively according to the "dependency" relationships among these tasks. An experimental system called CARDS has been implemented to test the feasibility of the proposed approach.

Fair exchange between two or more potentially mutually distrusted parties has been identified as an important issue in electronic commerce. However, the correctness (fairness) of the existing fair exchange protocols that use a trusted third party (TTP) is based on the assumption that, during an exchange, there are no failures at any of the local systems involved in the exchange, which is too strong in many situations. This paper points out that (1) system failures could cause loss of fairness, and (2) existing fair exchange protocols that use TTPs cannot ensure fairness in presence of system failures. We present a systematic way to develop such data exchange systems that can recover from system failures without losing fairness. We identify a set of fairness loss risks caused by local system failures. We identify a fault-tolerance correctness criterion for fair data exchange, denoted "fairness-lossless recoverability". A fairness-lossless recoverable fair exchange system is immune from the set of fairness loss risks. Standard message logging approaches are then studied and extended to achieve fairness-lossless recoverability with good performance.