Billions of dollars are lost every year in DeFi platforms by transactions exploiting business logic or accounting vulnerabilities. Existing defenses focus on static code analysis, public mempool screening, attacker contract detection, or trusted off-chain monitors, none of which prevents exploits submitted through private relays or malicious contracts that execute within the same block. We present the first decentralized, fully on-chain learning framework that: (i) performs gas-prohibitive computation on Layer-2 to reduce cost, (ii) propagates verified model updates to Layer-1, and (iii) enables gas-bounded, low-latency inference inside smart contracts. A novel Proof-of-Improvement (PoIm) protocol governs the training process and verifies each decentralized micro update as a self-verifying training transaction. Updates are accepted by PoIm only if they demonstrably improve at least one core metric (e.g., accuracy, F1-score, precision, or recall) on a public benchmark without degrading any of the other core metrics, while adversarial proposals get financially penalized through an adaptable test set for evolving threats. We develop quantization and loop-unrolling techniques that enable inference for logistic regression, SVM, MLPs, CNNs, and gated RNNs (with support for formally verified decision tree inference) within the Ethereum block gas limit, while remaining bit-exact to their off-chain counterparts, formally proven in Z3. We curate 298 unique real-world exploits (2020 - 2025) with 402 exploit transactions across eight EVM chains, collectively responsible for $3.74 B in losses. We demonstrate that on-chain ML governed by PoIm detects previously unseen attacks with over 97% attack detection accuracy and 82.0% F1. A single inference, such as one made via an external call, typically incurs zero cost. Fully on-chain inference consumes 57,603 gas (≈ $0.18) for linear models, 143,647 gas (≈ $0.49) for CNN(F2, K1), and 506,397 gas (≈ $1.77) for CNN(F8, K4) on L1 (e.g., Ethereum). Our results show that practical and continually evolving DeFi defenses can be embedded directly in protocol logic without trusted guardians, and our solution achieves highly cost-effective protection while filling a critical gap between vulnerability scanners and real-time transaction screening.

Smart contracts in Decentralized Finance (DeFi) platforms are attractive targets for attacks as their vulnerabilities can lead to massive amounts of financial losses. Flash loan attacks, in particular, pose a major threat to DeFi protocols that hold a Total Value Locked (TVL) exceeding 106 billion. These attacks use the atomicity property of blockchains to drain funds from smart contracts in a single transaction. While existing research primarily focuses on price manipulation attacks, such as oracle manipulation, mitigating non-price flash loan attacks that often exploit smart contracts' zero-day vulnerabilities remains largely unaddressed. These attacks are challenging to detect because of their unique patterns, time sensitivity, and complexity. In this paper, we present FlashGuard, a runtime detection and mitigation method for non-price flash loan attacks. Our approach targets smart contract function signatures to identify attacks in real-time and counterattack by disrupting the attack transaction atomicity by leveraging the short window when transactions are visible in the mempool but not yet confirmed. When FlashGuard detects an attack, it dispatches a stealthy dusting counterattack transaction to miners to change the victim contract's state which disrupts the attack's atomicity and forces the attack transaction to revert. We evaluate our approach using 20 historical attacks and several unseen attacks. FlashGuard achieves an average real-time detection latency of 150.31ms, a detection accuracy of over 99.93%, and an average disruption time of 410.92ms. FlashGuard could have potentially rescued over \405.71 million in losses if it were deployed prior to these attack instances. FlashGuard demonstrates significant potential as a DeFi security solution to mitigate and handle rising threats of non-price flash loan attacks.

Flash loan attacks threaten decentralized finance (DeFi) protocols, which constitute a Total Value Locked (TVL) of more than 106 billion. These attacks exploit the atomicity property in blockchains to drain funds within a single block. Existing research overlooks the mitigation of non-price flash loan attacks, which mostly exploit zero-day vulnerabilities. These attacks are challenging to detect as they are highly time-sensitive and each instance of the attack is complex and has a unique pattern. To address this challenge, we present FlashGuard, a runtime detection and mitigation framework for non-price flash loan attacks. FlashGuard communicates directly with the miners and bypasses the public mempool, where attack transactions usually reside. We utilize the temporary time window where transactions are visible in the mempool but not yet confirmed. Once the attack is detected, FlashGuard dispatches a dusting counter-transaction for the victim contract to the miners directly within the same block to disrupt the attack's atomicity and change the smart contract state. This forces the malicious transaction to revert. FlashGuard ensures that the series of operations that are required for a non-price flash loan attack cannot be completed atomically, leading to a failure of the attack. Our evaluation using 20 historical attacks that exploited protocol vulnerabilities shows an outstanding detection rate for FlashGuard with minimal false positives, and effective attack disruption and indicates that FlashGuard could have rescued about $405.71 million in losses.

Machine Learning (ML)-based algorithms have been widely adopted in communication networking optimization problems However, many studies that utilize ML-based approaches often overlook the comparison between ML algorithms and traditional, heuristic algorithms. In this paper, we study the merits and downsides of ML-based algorithms in a Software Defined Networking (SDN) routing scenario by analyzing a Deep Reinforcement Learning (DRL) routing algorithm assisted by a Graph Neural Network (GNN). The performances of the ML and traditional routing algorithms are evaluated in different network topologies. We consider a novel network reliability metric as well. We observe that traditional routing algorithms provide comparable performance to ML.

Next generation cellular networks are expected to enable a wide range of new applications, increasing societal dependence on the network infrastructure and requiring a higher level of resilience than current networks. In this paper, we consider the challenges network operators face in providing end-to-end connections across the backhaul part of the cellular network in the face of equipment failures and power outages. In particular, we discuss the impact of the move to commodity hardware, disaggregation of the radio access network, edge computing, densification of the network, and the increased electric power requirements on resilience. Techniques and research directions for overcoming the challenges are presented. This includes thinking beyond methods for a single network operator including cooperative operator techniques and extending resilient overlays to the wireless edge.

On the Internet of Things (IoT), devices do not have the required computational power and storage capacity; and as a result, a variety of IoT devices may be required to outsource sensed or generated data to multiple heterogeneous cloud servers. We posit that it is the Data Owner's responsibility to verify whether the stored data remain unchanged when the owner or some trusted third party further requires accessing this data. However, the "level" of this verification may be different under different contexts based on the application need. We propose four methods of integrity verification (which we call the ambit of data integrity - ADI) that considers the "toll" in terms of time, storage and communication by enlisting typically disparate integrity approaches under a single orbit. We adapt the notion of contextual integrity, previously used for assessing privacy grants, to extract important parameters required to decide on a suitable data integrity verification process. We propose a secure architecture using an integration of software defined perimeter (SDP) and software defined network (SDN) to perform authentication and gather each partition's context information for an SDN application to decide the proper integrity verification method that addresses the context requirements. To the best of our knowledge, this is the first time that the scope of integrity (or the data context) is used to determine the required layer of integrity verification in IoT.

Recent advances in information technologies have facilitated applications to generate, collect or process large amounts of sensitive personal data. Emerging cloud storage services provide a better paradigm to support the needs of such applications. Such cloud based solutions introduce additional security and privacy challenges when dealing with outsourced data including that of supporting fine-grained access control over such data stored in the cloud. In this paper, we propose an integrated, privacy-preserving user-centric attribute based access control framework to ensure the security and privacy of users' data outsourced and stored by a cloud service provider (CSP). The core component of the proposed framework is a novel privacy-preserving, revocable ciphertext policy attribute-based encryption (PR-CP-ABE) scheme. To support advanced access control features like write access on encrypted data and privacy-preserving access policy updates, we propose extended Path-ORAM access protocol that can also prevent privacy disclosure of access patterns. We also propose an integrated secure deduplication approach to improve the storage efficiency of CSPs while protecting data privacy. Finally, we evaluate the proposed framework and compare it with other existing solutions with regards to the security and performance issues.

Developing automated trust mechanisms has become crucial for overcoming perceptions of uncertainty and risk by people using IoT services. Things are increasingly communicating with each other and trust in the data they deliver depends on several factors such as the links they use to communicate and the environment. This points to a need for a trust management method for "things" that considers the communication among them, environmental and security-related factors, and the net-work topology but without human intervention. To address these challenges, we propose a trust management framework that automatically computes the trust of "things". We use Multi-Attribute Decision Making (MADM) and Evidence-Based Subjective Logic (EBSL) in a trust network of "things" to take into account the uncertainty in trust values. We propose new normalization for non-monotonic attributes in MADM. We present an algorithm for automatic trust computation and evaluate its effectiveness using synthetic data and sampling from real datasets.

Eavesdroppers can exploit exposed packet headers towards attacks that profile clients and their data flows. In this paper, we propose FOG, a framework for effective full and partial header blinding using MIMO, to thwart eavesdroppers. FOG effectively tracks header bits as they traverse physical (PHY) layer sub-systems that perform functions like scrambling and interleaving. It combines multiple blinding signals for more effective and less predictable obfuscation, as compared to using a fixed blinding signal. We implement FOG on the WARP platform and demonstrate via extensive experiments that it yields better obfuscation than prior schemes that deploy full packet blinding. It causes a bit error rate (BER) of > 40 % at an eavesdropper if two blinding streams are sent during header transmissions. Furthermore, even with full header blinding, FOG incurs a very small throughput hit of ≈5% with one blinding stream (and 9 % with two streams). Full packet blinding incurs much higher throughput hits (25 % with one stream and 50 % with two streams).

The increased connectivity introduced in Internet of Things (IoT) applications makes such systems vulnerable to serious security threats. In this paper, we consider one of the most challenging threats in IoT networks, where devices manipulate (maliciously or unintentionally) the data transmitted in information packets as they are being forwarded from the source to the destination. We propose unsupervised learning that exploits network diversity to detect and identify suspicious networked elements. Our proposed method can identify suspicious nodes along multihop transmission paths and under variable attack levels within the network. More specifically, we formulate a contribution metric for each networked element, which is used as a feature to cluster the nodes based on their behavior. We proposed two detection approaches, namely hard detection and soft detection. In the former, nodes are clustered into malicious or benign group; while in the latter, nodes are clustered into three groups based on their suspicious level, then highly suspicious nodes are discarded and more accurate contribution features are evaluated for the remaining nodes. Soft detection has higher detection accuracy provided that there is sufficient network diversity. Simulation results show that the proposed methods achieve high detection accuracy under different percentages of malicious nodes in the network and in the existence of channel errors.

Eavesdroppers can exploit exposed packet headers towards attacks that profile clients and their data flows. In this paper, we propose FOG, a framework for effective header blinding using MIMO, to thwart eavesdroppers. FOG effectively tracks header bits as they traverse physical (PHY) layer sub-systems that perform functions like scrambling and interleaving. It combines multiple blinding signals for more effective and less predictable obfuscation, as compared to using a fixed blinding signal. We implement FOG on the WARP platform and demonstrate via extensive experiments that it yields better obfuscation than prior schemes that deploy full packet blinding. It causes a bit error rate (BER) of > 40 % at an eavesdropper if two blinding streams are sent during header transmissions. Furthermore, FOG incurs a very small throughput hit of ≈5 % with one blinding stream (and 9 % with two streams). Full packet blinding incurs much higher throughput hits (25 % with one stream and 50 % with two streams).

In the age of Big Data, we are witnessing a huge proliferation of digital data capturing our lives and our surroundings. Data privacy is a critical barrier to data analytics and privacy-preserving data disclosure becomes a key aspect to leveraging large-scale data analytics due to serious privacy risks. Traditional privacy-preserving data publishing solutions have focused on protecting individual's private information while considering all aggregate information about individuals as safe for disclosure. This paper presents a new privacy-aware data disclosure scheme that considers group privacy requirements of individuals in bipartite association graph datasets (e.g., graphs that represent associations between entities such as customers and products bought from a pharmacy store) where even aggregate information about groups of individuals may be sensitive and need protection. We propose the notion of εg-Group Differential Privacy that protects sensitive information of groups of individuals at various defined group protection levels, enabling data users to obtain the level of information entitled to them. Based on the notion of group privacy, we develop a suite of differentially private mechanisms that protect group privacy in bipartite association graphs at different group privacy levels based on specialization hierarchies. We evaluate our proposed techniques through extensive experiments on three real-world association graph datasets and our results demonstrate that the proposed techniques are effective, efficient and provide the required guarantees on group privacy.

Traditional privacy-preserving data disclosure solutions have focused on protecting the privacy of individual's information with the assumption that all aggregate (statistical) information about individuals is safe for disclosure. Such schemes fail to support group privacy where aggregate information about a group of individuals may also be sensitive and users of the published data may have different levels of access privileges entitled to them. We propose the notion ofεg-Group Differential Privacy that protects sensitive information of groups of individuals at various defined privacy levels, enabling data users to obtain the level of access entitled to them. We present a preliminary evaluation of the proposed notion of group privacy through experiments on real association graph data that demonstrate the guarantees on group privacy on the disclosed data.

Cellular network virtualization is being considered as a key trend in future mobile networks towards improved resource utilization. However, virtualization scenarios need investigation to understand the considerations which should be taken into account when deploying virtualized wireless networks in practice. Towards this, we address the performance of a virtualized network in the presence of heterogeneous classes of traffic. In previous cellular network virtualization literature, both Real time (RT) and Non-Real time (NRT) traffic requests have been included without distinction. Both types are provisioned using the same algorithm for allocation of resources specified by the Network Scheduler [1]. However, different types of traffic have different characteristics [2], e.g., RT requests are delay sensitive but may need fixed bandwidth, and hence should be treated differently, especially when wireless channel conditions are factored into the scheduling. We recognize this difference and in this paper, we propose a new approach to improve scheduling of resources for RT and NRT traffic. In particular, we prioritize the traffic belonging to different virtual slices from all service providers (SP/VEs) at the Network Scheduler before allocating resources to different SP/VEs, i.e., We form a Virtual Prioritized Slice (VPS). The virtual prioritized slice is forwarded to the VPS scheduler to serve all RT requests first. Only after the RT traffic is scheduled, the NRT traffic is provisioned using proportional fairness (PF) scheduling. We show by simulation results that this new VPS approach helps outperform recently proposed resource allocation schemes.

Cellular data networks are proliferating to address the need for ubiquitous connectivity. To cope with the increasing number of subscribers and with the spatiotemporal variations of the wireless signals, current cellular networks use opportunistic schedulers, such as the Proportional Fairness scheduler (PF), to maximize network throughput while maintaining fairness among users. Such scheduling decisions are based on channel quality metrics and Automatic Repeat reQuest (ARQ) feedback reports provided by the User's Equipment (UE). Implicit in current networks is the a priori trust on every UE's feedback. Malicious UEs can, thus, exploit this trust to disrupt service by intelligently faking their reports. This work proposes a trustworthy version of the PF scheduler (called TPF) to mitigate the effects of such Denial-of-Service (DoS) attacks. In brief, based on the channel quality reported by the UE, we assign a probability to possible ARQ feedbacks. We then use the probability associated with the actual ARQ report to assess the UE's reporting trustworthiness. We adapt the scheduling mechanism to give higher priority to more trusted users. Our evaluations show that TPF 1) does not induce any performance degradation under benign settings, and 2) it completely mitigates the effects of the activity of malicious UEs. In particular, while colluding attackers can obtain up to 77 percent of the time slots with the most sophisticated attack, TPF is able to contain this percentage to as low as 6 percent.

Virtualizing wireless networks has the potential to improve resource usage efficiency (system capacity) through spectrum sharing while allowing for isolation between users and customization of applications {9}. In most work related to wireless network virtualization, the sharing of spectrum is considered at the level of chunks of frequency that do not interfere. Such spectrum sharing, where service provider SPA can use the spectrum allocated to SPB when SPB does not use it, results in multiplexing gains improving the resource usage (see for example, [12]). We argue that sharing radio resources that are a function of geography and signal strength, rather than slices of spectrum is also possible. When we consider sharing of radio resources, the transmit power, the interference, and the usage scenario (capabilities/needs of devices) become important in determining what can be shared. In this paper, the potential gain from sharing such radio resources while using MIMO for combating interference and exploiting spatial degrees of freedom is investigated in a two service provider collaboration scenario. The metric used is the capacity of the system (with a large cell and a small cell) as a function of separation distance, transmit power, cell range, and various MIMO settings. We show that radio resource sharing is feasible, but it has implications on isolation between users of different SPs and MIMO settings are an important factor.

Cellular data networks are proliferating to address the need for ubiquitous connectivity. To cope with the increasing number of subscribers and with the spatio-temporal variations of the wireless signals, current cellular networks use opportunistic schedulers, such as the Proportional Fairness scheduler (PF), to maximize network throughput while maintaining fairness among users. Such scheduling decisions are based on channel quality metrics and Automatic Repeat reQuest (ARQ) feedback reports provided by the User's Equipment (UE). Implicit in current networks is the a priori trust on every UE's feedback. Malicious UEs can thus exploit this trust to disrupt service by intelligently faking their reports. This work proposes a trustworthy version of the PF scheduler (called TPF) to mitigate the effects of such Denial-of-Service (DoS) attacks. In brief, based on the channel quality reported by the UE, we assign a probability to possible ARQ feedbacks. We then use the probability associated with the actual ARQ report to assess the UE's trustworthiness. We adapt the scheduling mechanism to give higher priority to more trusted users. Our evaluations show that TPF (i) does not induce any performance degradation under benign settings, and (ii) it completely mitigates the effects of the activity of malicious UEs.

The proliferation of Location-based Social Networks (LBSNs) has been rapid during the last year due to the number of novel services they can support. The main interaction between users in an LBSN is location sharing, which builds the spatial component of the system. The majority of the LBSNs make use of the notion of check-in, to enable users to volunteeringly share their whereabouts with their peers and the system. The flow of this spatial information is unidirectional and originates from the users' side. Given that currently there is no infrastructure in place for detecting fake checkins, the quality of the spatial information plane of an LBSN is solely based on the honesty of the users. In this paper, we seek to raise the awareness of the community for this problem, by identifying and discussing the effects of the presence of fake location information. We further present a preliminary design of a fake check-in detection scheme, based on location-proofs. Our initial simulation results show that if we do not consider the infrastructural constraints, location-proofs can form a viable technical solution.

An approach advocated in the recent literature for reducing energy consumption in cellular networks is to put base stations to sleep when traffic loads are low. However, several practical considerations are ignored in these studies. In this paper, we aim to raise questions on the feasibility and benefits of base station sleeping. Specifically we analyze the interference and capacity of a coverage-based energy reduction system in CDMA based cellular networks using a simple analytical model and show that sleeping may not be a feasible solution to reduce energy consumption in many scenarios.

The laborious collection of location fingerprints, that could also potentially change with time, remains a hurdle towards the widespread deployment of indoor and campus area positioning using WiFi. In this paper, we present a preliminary analysis of the complicated relationship between distance in signal space, the physical distance and location errors towards better guidelines for fingerprint collection. We introduce the idea of entropy of location fingerprints and investigate the relationships between physical and signal distances, entropy, and expected errors with positioning using location fingerprinting. We present results with no access point and one access point and consider variations in the density of reference points and the standard deviation of signal strength to illustrate the issues.

Heterogeneous conditions can occur in multi-hop wireless networks due to a variety of factors such as variations in transmission power and signal propagation environments. Directed links can occur when the environment and/or the nodes are heterogeneous. In this paper, we examine the network connectivity for heterogeneous multi-hop wireless networks and propose an algorithm to identify the connectivity of the network. We follow this with a numerical study of the connectivity in random topologies. Lastly, we propose two schemes for constructing additional links to enhance the connectivity of the network. Our proposed schemes identify the links to be improved or created via a cluster based approach.

Selecting high performance routes in wireless networks requires the exchange of link quality information among nodes. Adversaries can manipulate this functionality by advertising fake qualities for links; by doing so, they can attract routes and subsequently launch pernicious attacks. Our measurements suggest that malicious route attraction can fatally impact throughput. We design a framework that is effective against both independent and colluding attackers. In the latter case, we consider both local and remote colluders. With local collusion, malicious nodes exchange and advertise fake routing information to increase the probability of being selected as relays. Remote collusion refers to nodes residing in distant parts of the network that (i) create sybil identities in a local neighborhood and / or (ii) utilize link quality reports to advertise fake links. Our framework combines packet signing and frequency hopping to accurately detect the adversaries. We implement the framework on our testbed and conduct experiments to assess its efficacy. We observe that our framework provides significant throughput benefits by detecting attackers with 90% accuracy.

Abstract The identity and/or location of communicating entities in wireless ad hocnetworks is extremely important due to the potential of their being identified and subsequently subjected to cyber or physical attacks. In this paper, we show that a global attacker who can eavesdrop on the overall data transmissions and count them can simply visualize the transmissions and infer contextual information. Current approaches to obfuscate the locations of source and destinations do not provide protection against such attacks. We propose two novel techniques (1) SECLOUD: Source and Destination Seclusion using Clouds to obfuscate the true source/destination nodes and make them indistinguishable among a group of neighbor nodes, and (2) ANONYRING: Anonymous Ring which hides the source/destination nodes within a group of nodes that form a ring. Both proposed techniques work well even under network‐wide traffic visualization by a global attacker. Furthermore the proposed techniques are shown viasimulation to be superior to existing schemes in the literature. Copyright © 2010 John Wiley & Sons, Ltd.

The inherent channel characteristics of impulse-based UWB networks affect the MAC layer performance significantly. Most previous studies on evaluating MAC protocols are based on prolonged simulations and do not account for the multiple access interference due to multipath delay spread. In this work, we develop CTU, an analytical framework for Capturing the Throughput dependencies in UWB networks, while taking into account the PHY layer effects. The key attributes of CTU are: 1) It is modular; it can be easily modified to provide a basis for evaluating a wide range of MAC protocols for impulse-based UWB networks. The only requirements are that the MAC protocol under study be based on time-hopping and the modulation scheme be pulse position modulation; these are common design decisions in UWB networks. 2) It considers the channel characteristics in addition to MAC layer effects; CTU correlates probabilistically the multipath delay profile of the channel with the packet error rate. We employ CTU to evaluate the performance of different generic medium access procedure. We compare the results with those from extensive simulations and show the high accuracy of CTU. We use CTU to assess the impact of various system parameters on the MAC layer performance; we make several interesting observations that are discussed in depth.

There are performance deficiencies that hamper the deployment of Wireless Sensor Networks (WSNs) in critical monitoring applications. Such applications are characterized by considerable network load generated as a result of sensing some characteristics of the monitored system. Excessive packet collisions lead to packet losses and retransmissions, resulting in significant overhead costs and latency. In order to address this issue, we introduce a distributed and scalable scheduling access scheme that mitigates high data loss in data-intensive sensor networks and can also handle some mobility. Our approach alleviates transmission collisions by employing virtual grids that adopt Latin Squares characteristics to time slot assignments. We show that our algorithm derives conflict-free time slot allocation schedules without incurring global overhead in scheduling. Furthermore, we verify the effectiveness of our protocol by simulation experiments. The results demonstrate that our technique can efficiently handle sensor mobility with acceptable data loss, low packet delay, and low overhead.

Recent work on cooperative communications has demonstrated benefits in terms of improving the reliability of links through diversity and/or increasing the reach of a link compared to a single transmitter transmitting to a single receiver (single-input single-output or SISO). In one form of cooperati

We propose a novel technique called dimming to improve the energy efficiency of cellular networks by reducing the capacity, services, and energy consumption of cells without turning off the cells. We define three basic methods to dim the network: coverage, frequency, and service dimming. We construct a multi-time period optimization problem to implement frequency dimming and extend it to implement both frequency and service dimming together. We illustrate the ability of dimming techniques to adapt the capacity and network services in proportion to the dynamic spatial and temporal load resulting in significant energy savings through numerical results for a sample network.

In this paper, we develop a performance modeling technique for analyzing the time varying network layer queueing behavior of multihop wireless networks with constant bit rate traffic. Our approach is a hybrid of fluid flow queueing modeling and a time varying connectivity matrix. Network queues are modeled using fluid-flow based differential equation models which are solved using numerical methods, while node mobility is modeled using deterministic or stochastic modeling of adjacency matrix elements. Numerical and simulation experiments show that the new approach can provide reasonably accurate results with significant improvements in the computation time compared to standard simulation tools.

Simple calibration-free localization techniques using proximity to multiple monitoring stations are cost-effective and avoid the need for laborious measurements, calibration, and large search spaces for RF fingerprints. Proximity-based localization does not suffer from multi-path problems associated with localization that employs time, time difference, or angle of arrival measurements. We propose Sub-Area Localization (SAL) that uses the sub-areas created by the overlapping ranges of monitoring stations (MoSs) to estimate the location of a mobile node. We investigate the relationship between localization accuracy (that depends on the number and sizes of sub-areas) and the monitoring ranges of MoSs when they are placed on a virtual grid in a given workspace. We present analytical and simulation results that allow us to determine the best range of monitoring stations, and understand the limits on the accuracy performance of SAL.

The ability to correctly determine their neighborhood is a fundamental requirement for nodes in ad hoc and sensor networks. Many applications, protocols, and system functionality rely on neighborhood discovery. Malicious nodes that taint neighborhood information using wormholes can significantly dis

Detecting malicious packet dropping is important in ad hoc networks to combat a variety of security attacks such as blackhole, greyhole, and wormhole attacks. We consider the detection of malicious packet drops in the presence of collisions and channel errors and describe a method to distinguish between these types. We present a simple analytical model for packet loss that helps a monitoring node to detect malicious packet dropping attacks. The model is analyzed and evaluated using simulations. The results show that it is possible to detect malicious packet drops in the presence of collisions and channel errors.

The privacy of communicating entities in wireless ad hoc networks is extremely important due to the potential of their being identified and subsequently subjected to attacks (e.g., in military networks). Previously, random walk and fractal propagation schemes have been proposed to address privacy of source and destination nodes in ad hoc or sensor networks. Entropy of packet transmissions has been used as the metric for comparison. In this paper, we show that under a global attacker that can eavesdrop on the overall data transmissions and count them, neither of these approaches provide sufficient privacy when the attacker can visualize the transmissions and infer contextual information. Moreover, we show that the entropy is not a useful metric in such a case. We propose SECLOUD: Source and Destination Seclusion using Clouds to obfuscate the true source/destination nodes and make them indistinguishable among a group of neighbor nodes which works well even under network-wide traffic visualization by a global attacker.

A well-known approach to increase the resilience of mobile ad hoc networks (MANETs) and unstructured sensor networks is to ensure a network topology where there are at least k disjoint routes in the network between each pair of network nodes (usually called k-connectivity). Asymptotic analyses of node density requirements for k-connectivity have been considered in the literature. In this paper, we present the results of a simulation study investigating the relationship between asymptotic results in the literature and k-connectivity under varying nodal density and nodal degree. The numerical results illustrate where the asymptotic approximations breakdown and we show that this largely due to the existence of critical connectivity points in the topology. Using a critical point identification algorithm we examine how the number of critical points varies with nodal degree, nodal density and node mobility. In addition, critical point is evaluated its effectiveness on the network caused by failure.

Jamming attacks are considered one of the most devastating attacks as they are difficult to prevent and sometimes hard to detect. In this paper we consider the impact of the placement and range of limited-range jammers on ad hoc networks. Limited range jammers are more difficult to detect as they use transmission powers similar to that of regular nodes (or perhaps even smaller transmit powers). The attacker can locate his jammer(s) randomly in the network. Alternatively, jammers can be placed at strategic locations. For instance, intuitively, this can be nodes with the highest traffic inputs/outputs (discovered by sensing the traffic flow in the network). Using OPNET, we perform extensive simulations to show how significant such strategically placed attacks can be compared to random placement of limited-range jammers on both TCP and UDP traffic.

The wormhole attack is considered to be a serious security attack in multihop ad hoc and sensor networks. We propose "DeWorm", a simple protocol to effectively detect wormhole attacks without the need for special hardware and/or strict location or synchronization requirements. DeWorm makes use of discrepancies in routing information between neighbors to detect wormholes. A simulation based analysis of DeWorm for a variety of scenarios shows that the proposed protocol can detect wormhole attacks with a high detection rate, a low false positive rate and low overhead. Further, in comparison to other wormhole detection protocols, the proposed protocol is simple, localized, and capable of detecting a variety of types of wormhole attacks including physical layer wormholes.

The inherent channel characteristics of impulse-based UWB networks affect the MAC layer performance significantly. Previous studies on evaluating MAC protocols are based on prolonged simulations, and do not account for the multiple-access interference that arises due to multipath delay spread. In this work, we develop CTU, an analytical framework that captures the performance of MAC protocols, while taking into account the underlying PHY layer effects. The key attributes that make CTU novel are: (a) It is modular and therefore flexible; it can be easily modified to provide a basis for characterizing and evaluating a wide range of MAC protocols designed for impulse-based UWB networks. The only requirements are that the MAC protocol under study be based on time-hopping, and the modulation scheme be pulse position modulation; these are common design decisions in most impulse based UWB networks, (b) It considers the channel characteristics in addition to MAC layer effects; in particular, CTU correlates probabilistically the multipath delay profile of the channel with the packet error rate. We employ CTU to evaluate the performance of a generic medium access procedure. We compare the results with those from extensive simulations and show the high accuracy of CTU. We use CTU to assess the impact of various system parameters on the MAC layer performance; we make several interesting observations that are discussed in depth.

We introduce a distributed grid-based scheduling access scheme that mitigates high data loss in data intensive sensor networks. Our approach alleviates transmission collisions by applying virtual grids and adopting Latin Squares Characteristic to time slot assignments. We demonstrate that our technique efficiently handles sensor mobility with acceptable data loss and low overhead.

Analytical models to evaluate and predict "precision" performance of indoor positioning systems based on location fingerprinting are lacking. Such models can be used to improve the design of positioning systems, for example by eliminating some fingerprints and reducing the size of the location fingerprint database. In this paper, we develop a new analytical model that employs proximity graphs for predicting performance of indoor positioning systems based on location fingerprinting. The model allows computation of an approximate probability distribution of error distance given a location fingerprint database based on received signal strength and its associated statistics. The performance results from the simulation and the analytical model are found to be congruent. This model also allows us to perform analysis of the internal structure of location fingerprints. We employ the analysis of the internal structure to identify and eliminate unnecessary location fingerprints stored in the database, thereby saving on computation while performing location estimation.

Cooperative diversity facilitates spatio-temporal communications without requiring the deployment of physical antenna arrays. While physical layer studies on cooperative diversity have been extensive, higher layer protocols which translate the achievable reduction in the SNR per bit for a given target BER, into system wide performance enhancements are yet to mature. The challenge is that appropriate higher layer functions are needed in order to enable cooperative diversity at the physical layer. We focus on network-wide broadcasting with the use of cooperative diversity in ad hoc networks. We design a novel distributed network-wide broadcasting protocol that takes into account the physical layer dependencies that arise with cooperative diversity. We perform extensive simulations that show that our protocol can outperform the best of the noncooperative broadcasting protocols by: (a) achieving up to a threefold increase in network coverage and, (b) by decreasing the latency incurred during the broadcast by about 50%. We also construct an analytical model that captures the behavior of our protocol. Furthermore, we show that computing the optimal solution to the cooperative broadcast problem is NP-complete and construct centralized approximation algorithms. Specifically, we construct an O(Nepsi)-approximation algorithm with a computational complexity of O(N4/epsi); we also construct a simpler greedy algorithm.. The costs incurred with these algorithms serve as benchmarks with which one can compare that achieved by any distributed protocol

Space-time communications can help combat fading and, hence, can significantly increase the capacity of ad hoc networks. Cooperative diversity or virtual antenna arrays facilitate spatio-temporal communications without actually requiring the deployment of physical antenna arrays. Virtual MISO entails the simultaneous transmission of appropriately encoded information by multiple nodes to effectively emulate a transmission on an antenna array. We present a novel multilayer approach for exploiting virtual MISO links in ad hoc networks. The approach spans the physical, medium access control and routing layers, and provides 1) a significant improvement in the end-to-end performance in terms of throughput and delay and 2) robustness to mobility and interference-induced link failures. The key physical layer property that we exploit is an increased transmission range due to achieved diversity gain. Except for space-time signal processing capabilities, our design does not require any additional hardware. We perform extensive simulations to quantify the benefits of our approach using virtual MISO links. As compared to using only SISO links, we achieve an increase of up to 150 percent in terms of the end-to-end throughput and a decrease of up to 75 percent in the incurred end-to-end delay. Our results also demonstrate a reduction in the route discovery attempts due to link failures by up to 60 percent, a direct consequence of the robustness that our approach provides to link failures

Space-time communications can help combat fading and hence can significantly increase the capacity of ad hoc networks. Cooperative diversity or virtual antenna arrays facilitate spatio-temporal communications without actually requiring the deployment of physical antenna arrays. Virtual MISO entails the simultaneous transmission of appropriately encoded information by multiple nodes to effectively emulate a transmission on an antenna array. We present a novel multi-layer approach for exploiting virtual MISO links in ad hoc networks. The approach spans the physical, medium access control and routing layers and provides: (a) a significant improvement in the end-to-end performance in terms of throughput and delay and, (b) robustness to mobility and interference induced link failures. The key physical layer property that we exploit is an increased transmission range due to achieved the diversity gain. Except for space-time signal processing capabilities, our design does not require any additional hardware. We perform extensive simulations to quantify the benefits of our approach using virtual MISO links. As compared to using only SISO links, we achieve an increase of up to 150% in terms of the end-to-end throughput and a decrease of up to 75% in the incurred end-to-end delay. Our results also demonstrate a reduction in the route discovery attempts due to link failures by up to 60%, a direct consequence of the robustness that our approach provides to link failures.

Data Intensive Mobile Sensor Networks (DIMSNs) introduce a promising but still under-utilized technology. Meanwhile, there is a growing confidence that certain applications (Killer Apps) have a potential to create a sustained market for this technology. For example, a large team of cooperative mobile robots can be considered as a wireless sensornet composed of a number of mobile nodes most of which are powerconstrained. Such mobile robots can be deployed in conjunction with stationary sensor nodes to acquire and process data for surveillance and tracking, environmental monitoring for highly sensitive areas, or execute search and rescue operations. This example illustrates conceptual attractiveness of the DIMSN systems that generates interesting and appealing research challenges (e.g., intelligent mobile agents, semantically enriched and contextaware wireless services, smart network monitoring infrastructures). However, while providing excellent funding opportunities, those challenges often underestimate the GRAND DETERRENTS that make moves towards practical data-intensive mobile sensornets extremely difficult.

In this paper we propose a query-driven approach for tuning the time/energy trade-off in sensor networks with mobile sensors. The tuning factors include re-positioning of mobile sensors and changing their transmission ranges. We propose an algebraic query optimization framework that explores these factors while utilizing collision-free concurrent data transmissions with different degrees of data filtering and aggregation.

In previous years, positioning systems for indoor areas using the existing wireless local area network infrastructure have been suggested. Such systems make use of location fingerprinting rather than time or direction of arrival techniques for determining the location of mobile stations. While experimental results related to such positioning systems have been presented, there is a lack of analytical models that can be used as a framework for designing and deploying the positioning systems. In this paper, we present an analytical model for analyzing such positioning systems. We develop the framework for analyzing a simple positioning system that employs the Euclidean distance between a sample signal vector and the location fingerprints of an area stored in a database. We analyze the effect of the number of access points that are visible and radio propagation parameters on the performance of the positioning system and provide some preliminary guidelines on its design.

Information assurance techniques employed in wired networks have limited direct applicability in wireless networks because of the unique aspects of wireless networks (e.g., user mobility, wireless communication channel, power conservation, limited computational power in mobile nodes, security at the link layer, etc.). The interaction between the components of information assurance, namely availability and security in a wireless network environment poses new challenges. In this article, we present a framework for understanding survivability and security in wireless network and also discuss the issues related to the interaction between survivability and security in hybrid wireless access networks.

Indoor positioning systems that make use of received signal strength based location fingerprints and existing wireless local area network infrastructure have recently been the focus for supporting location-based services in indoor and campus areas. A knowledge and understanding of the properties of the location fingerprint can assist in improving design of algorithms and deployment of position location systems. However, most existing research work ignores the radio signal properties. This paper investigates the properties of the received signal strength reported by IEEE 802.11b wireless network interface cards. Analyses of the data are performed to understand the underlying features of location fingerprints. The performance of an indoor positioning system in terms of its precision is compared using measured data and a Gaussian model to see how closely a Gaussian model may fit the measured data.

Several distributed QoS mechanisms have been proposed to augment the existing distributed coordination function of the IEEE 802.11 MAC protocol to provide QoS support for QoS-sensitive applications. These mechanisms use well-known QoS enabling techniques such as priority assignment and fair scheduling within existing 802.11 MAC parameters. Although these mechanisms provide differentiated throughput for different classes of traffic, they cannot provide both relative and absolute throughput support simultaneously.In this paper, we propose a new mechanism called A-DRAFT that supports both absolute and relative throughput in an adaptive and a fully distributed manner. This paper describes an adaptive mechanism that supports absolute throughput as long as the total demand from this class is below the effective channel capacity. The proposed mechanism also provides relative or fair throughput support with low variation and a high degree of fairness even in a saturated network with a large number of MSs. We make use of deficit round robin scheduling with different levels of quantum rate to provide fairness, and different weights to provide absolute and relative throughput. We evaluate the performance of the proposed mechanism via mathematical analysis and confirm this analysis with simulations.

Rate and power control are extremely important in determining the quality of service and radio resource utilization in mobile data networks. In the literature, the rate is assigned such that the system throughput is maximized and the transmit power at the mobile station (MS) is controlled in order to maintain a signal to interference ratio (SIR) that can provide a 1% frame error rate at the base station (BS). When the objective of radio resource allocation is the maximization of the system throughput, all MSs do not obtain the same rates. The current CDMA standard (cdma2000-1x-EV-DO) employs rate control in a probabilistic way. The rate assignment may not result in maximizing the system throughput and the interference at the BS may exceed the allowable levels. Power control based on a fixed target SIR that was suitable for voice cellular networks is still used in the emerging mobile data networks in spite of the different requirements of voice and data MSs. In this work, we propose new approaches of rate and power control that can provide high throughput and completely utilize the radio resources while overcoming these drawbacks. The proposed schemes attempt to maximize the throughput of each MS while staying within the maximum allowable interference. Each MS gets the same transmission rate. Moreover, the scheme can be implemented in practice unlike other theoretical approaches. The proposed scheme shows better throughput and radio resource utilization than the traditional schemes of 1x-EV-DO systems.

Abstract In order to understand the behaviour of upper‐layer protocols and to design or fine tune their parameters over wireless networks, it is common to assume that the underlying channel is a flat Rayleigh fading channel. Such channels are commonly modeled as finite state Markov chains. Recently, hidden Markov models have also been employed to characterize these channels. In this paper, we study the different models that have been proposed along with the analysis of their validity. We start by presenting some preliminary concepts related to the modeling of the wireless communications channel. We then proceed to introduce finite state Markov channel models (FSMCs) along with the relations between them and the modulation schemes, error control protocols and channel coding. We propose and study the effects of taking into account the fading process in its characterization. We finish with a discussion on hidden Markov models for Rayleigh fading channel modeling. Copyright © 2004 John Wiley & Sons, Ltd.

It is well known that the distributed coordination function (DCF) of the IEEE 802.11 MAC protocol is not suitable for supporting multimedia and QoS-sensitive applications because of its inherent lack of QoS support and fairness. Recently, several distributed QoS mechanisms have been proposed which translate user QoS requirements into typically a single parameter of the DCF protocol. In this paper, we compare the pros and cons of the major distributed QoS mechanisms, and propose a new mechanism that provides superior performance and supports two different QoS models. The proposed mechanism is based on deficit round robin scheduling and translates the user throughput requirements into the 802.11 MAC interframe space and backoff interval parameters. We show via simulations that the proposed mechanism provides low variability of throughput and delay and has the advantage of low complexity.

Encryption algorithms are known to be computationally intensive. They consume a significant amount of computing resources such as CPU time, memory, and battery power. A wireless device, usually with very limited resources, especially battery power, is subject to the problem of energy consumption due to encryption algorithms. Designing energy efficient security protocols first requires an understanding of and data related to the energy consumption of common encryption schemes. In this paper, we provide the results of experiments with AES and RC4, two symmetric key algorithms that are commonly suggested or used in WLANs. Our results show that RC4 is more suitable for large packets and AES for small packets.

Power control has been extensively studied in cellular networks with primarily voice traffic. Next generation wireless networks are evolving towards all-data systems. In particular, transmission from mobile stations (MSs) will tend to be bursty and there will be a variety of transmission rates (multi-rate transmission). Recently, non-cooperative game theory has been applied to study power control in wireless data networks. In these studies, MSs are assumed to be transmitting continuously and all of them at the same rate. We apply non-cooperative game theory to bursty transmissions and multi-rate systems in this paper. This results in a power control game of incomplete information. The issues arising from this change are investigated and numerical results are provided.

In this paper, the problem of predicting the quality of MPEG video streams received over a best effort network is studied. The objective of the study is to develop and validate methods to accurately predict the video quality from network level quality of service (QoS) measurements to the application layer QoS. This involves QoS mapping between the various protocol layers as well as accounting for the frame dependencies in the MPEG video compression scheme. The validation is conducted using simulation with real video traces.

Location aware services are becoming attractive with the deployment of next generation wireless networks and broadband multimedia wireless networks especially in indoor and campus areas. To provide location aware services, obtaining the position of a user accurately is important. While it is possible to deploy additional infrastructure for this purpose, using existing communications infrastructure is preferred for cost reasons. Because of technical restrictions, location fingerprinting schemes are the most promising. In this paper we present a systematic study of the performance tradeoff and deployment issues. In this paper we present some experimental results towards such a systematic study and discuss some issues related to the indoor positioning problem.

Location aware handoff algorithms have a much better performance than traditional algorithms in terms of the number of handoffs and handoff delay (crossover point). As they do not pay attention to the received signal strength (RSS), it is not clear if they also maintain the best throughput in the case of wireless data networks. We evaluate the tradeoffs between unnecessary handoffs, handoff delay and system throughput. The results indicate that, with location-aware handoff algorithms, the throughput is also higher than the throughput in the case of using traditional RSS-based algorithms.

Because of the channel's characteristics the use of TCP over wireless may result in poor performance. Significant throughput degradation occurs when TCP interprets high bit error rate and mobile host disconnections as congestion in the network. We address this problem and focus our research on the study of how to improve the TCP's performance over wireless. We study TCP communications between mobile hosts communicating with each other using an infrastructure network. We introduce a new scheme, called MAITE(Mobility Awareness Incorporated as TCP Enhancement) that consists of an implementation of link layer messages that inform the TCP of high BER and disconnection conditions. By the use of simulations we compare the results obtained with MAITE with other previously proposed schemes. Our scheme provides us with improvements in transfer time of 30% over other schemes.

Multiple factors are contributing to the surge in the demand for wireless LAN (WLAN): laptop penetration, increased user mobility, ubiquitous access to the Internet and intranets, and newly introduced voice over IP (VoIP) capabilities. Today almost all popular wireless LAN applications such as Web access, E-mail processing, telnet, FTP, and database access are using a client-server architecture. However, the number of supported mobile nodes depends on the type of application the user employs. Thus, evaluation of the number of voice users supported by a wireless LAN access point is beginning to attract the attention of those involved in deployment of wireless LAN. Currently, most commercial wireless data networks are arbitrarily installed. The only optimization, if any, is that for the area covered by an access point. However the coverage aspect of the network design is only suitable for the initial installation phases wherein the capacity of users and the channel utilization is low. We investigate QoS parameters necessary to support voice and the implications these parameters have on WLAN design.

Traditional handoff algorithms based on stochastic and heuristic models perform poorly as they do not account for the rapid changes in the radio environments. Adaptive handoff algorithms are being developed to solve this problem with a corresponding tradeoff in terms of computational complexity. This paper suggests a location-aware handoff decision algorithm. The performance of the traditional and proposed handoff algorithm in a cellular digital packet data network (CDPD) are compared using simulations. The results indicate that the location-aware algorithm performs better than the traditional handoff algorithm.

The authors consider an anti-multipath modulation scheme called commutation signalling. The anti-multipath capabilities are studied by the use of computer simulations. In particular, multipath Rayleigh fading channels and multipath non-fading channels are considered with the assumption of a knowledge of the multipath delays at the receiver. The effects of correlation noise as the number of paths increases and the advantages or disadvantages of using the weaker multipath components in decision making are presented.