Machine learning (ML) is promising in accurately detecting malicious flows in encrypted network traffic; however, it is challenging to collect a training dataset that contains a sufficient amount of encrypted malicious data with correct labels. When ML models are trained with low-quality training data, they suffer degraded performance. In this paper, we aim to address a real-world low-quality training dataset problem, namely, detecting encrypted malicious traffic generated by continuously evolving malware. We develop RAPIER+ that fully utilizes different distributions of normal and malicious traffic data in the feature space, where normal data is tightly distributed in a certain area, and the malicious data is scattered over the entire feature space to augment training data for model training. RAPIER+ includes two pre-processing modules to convert traffic into feature vectors and correct label noises. We evaluate our system on two public datasets and one combined dataset. With 1000 samples and 45% noise from each dataset, our system achieves the F1 scores of 0.78, 0.84, and 0.87, respectively, achieving average improvements of 358.5%, 314.0%, and 221.1% over the existing methods, respectively. Furthermore, we evaluate RAPIER+ with a real-world dataset obtained from a security enterprise. RAPIER+ effectively achieves encrypted malicious traffic detection with the best F1 score of 0.81 and improves the F1 score of existing methods by an average of 288.7%.

With the widespread deployment of deep neural network (DNN) models, dynamic watermarking techniques are being used to protect the intellectual property of model owners.However, recent studies have shown that existing watermarking schemes are vulnerable to watermark removal and ambiguity attacks.Besides, the vague criteria for determining watermark presence further increase the likelihood of such attacks.In this paper, we propose a secure DNN watermarking scheme named ChainMarks, which generates secure and robust watermarks by introducing a cryptographic chain into the trigger inputs and utilizes a two-phase Monte Carlo method for determining watermark presence.First, ChainMarks generates trigger inputs as a watermark dataset by repeatedly applying a hash function over a secret key, where the target labels associated with trigger inputs are generated from the digital signature of model owner.Then, the watermarked model is produced by training a DNN over both the original and watermark datasets.To verify watermarks, we compare the predicted labels of trigger inputs with the target labels and determine ownership with a more accurate decision threshold that considers the classification probability of specific models.Experimental results show that ChainMarks exhibits higher levels of robustness and security compared to state-of-the-art watermarking schemes.With a better marginal utility, ChainMarks provides a higher probability guarantee of watermark presence in DNN models with the same level of watermark accuracy.

Traditional methods for identifying and mitigating cyber attacks are becoming inadequate due to ever-increasing volumes of network traffic, the complexity of modern cyber threats, and the use of encryption to protect payloads. This paper presents CyberMALT, a novel approach designed to address these challenges through machine learning-assisted analysis of traffic metadata, which provides valuable insights into network behavior without examining payloads. Our proposed solution utilizes a two-stage approach. First, we employ unsupervised machine learning techniques to study typical network behavior. This initial stage allows CyberMALT to establish a baseline understanding of typical traffic characteristics, enabling it to identify deviations indicative of potential threats. Leveraging this knowledge, CyberMALT computes an anomaly score for each observed traffic instance, thereby pinpointing suspicious activity for further investigation. In the second stage of processing, these identified anomalies undergo a comprehensive analysis to classify the types of attacks accurately and efficiently and rule out false positives. By leveraging machine learning for traffic metadata analysis, CyberMALT offers a proactive and adaptive solution for cyber threat detection and classification. Our experiments demonstrate the effectiveness of CyberMALT in identifying and classifying diverse cyber threats while minimizing false positives, thus enhancing the security posture of networked systems.

Path MTU Discovery (PMTUD) and IP address sharing are integral aspects of modern Internet infrastructure. In this paper, we investigate the security vulnerabilities associated with PMTUD within the context of prevalent IP address sharing practices. We reveal that PMTUD is inadequately designed to handle IP address sharing, creating vulnerabilities that attackers can exploit to perform off-path TCP hijacking attacks. We demonstrate that by observing the path MTU value determined by a server for a public IP address (shared among multiple devices), an off-path attacker on the Internet, in collaboration with a malicious device, can infer the sequence numbers of TCP connections established by other legitimate devices sharing the same IP address. This vulnerability enables the attacker to perform off-path TCP hijacking attacks, significantly compromising the security of the affected TCP connections. Our attack involves first identifying a target TCP connection originating from the shared IP address, followed by inferring the sequence numbers of the identified connection. We thoroughly assess the impacts of our attack under various network configurations. Experimental results reveal that the attack can be executed within an average time of 220 seconds, achieving a success rate of 70%. Case studies, including SSH DoS, FTP traffic poisoning, and HTTP injection, highlight the threat it poses to various applications. Additionally, we evaluate our attack across 50 real-world networks with IP address sharing---including public Wi-Fi, VPNs, and 5G---and find 38 vulnerable. Finally, we responsibly disclose the vulnerabilities, receive recognition from organizations such as IETF, Linux, and Cisco, and propose our countermeasures.

Most existing DL-based encrypted traffic classification methods suffer performance degradation in real-world deployments due to dynamic network conditions, e.g., network environment changes and traffic obfuscation. Dynamic network conditions cause encrypted traffic to exhibit distinct feature patterns during training and testing phases. To address this issue, we propose MetaTraffic, a novel and general DL training framework built upon meta-learning that enhances the performance of supervised DL models designed for encrypted traffic classification against dynamic network conditions. Our key observation is that the traffic of the same network behaviors share the same semantic features even under different network conditions, which can be considered as stable feature representations. Therefore, MetaTraffic helps DL models learn stable feature representations by minimizing the discrepancies in how the models represent traffic features under different network conditions, thereby achieving robust classification under dynamic network conditions. We implement MetaTraffic based on meta-learning with three innovative facilitate modules to enhance its performance. We evaluate MetaTraffic using three public datasets and three new large-scale encrypted traffic datasets that cover multiple types of network conditions. Experimental results show that, under dynamic multiple types of network conditions, our framework improves the accuracy of DL models by 8.94% and the F1-Macro score by 12.55%, while existing robust training methods decrease the accuracy by 28.85% and the F1-Macro score by 33.52%.

With the increasing adoption of confidential computing, security-sensitive applications are often deployed in confidential virtual machines (CVMs), which reduce reliance on third-party cloud providers. However, privilege attacks originating from the OS remain a significant threat in these environments. Existing finer-grained isolation schemes, such as SHELTER (USENIX SEC’23), provide process-level protection but are still vulnerable to intra-process attacks and potential collusion between the OS and intra-process adversaries. Many current intra-process isolation techniques continue to depend on the OS to manage and enforce isolation domains, leading to a large Trusted Computing Base (TCB). This gap highlights the need for more granular, less trust-dependent confidential computing solutions. In this paper, we present CCAegis, a system that extends the Arm Confidential Compute Architecture (CCA) to enforce intra-process isolation of sensitive data and operations, safeguarding them from both intra-process adversaries and the OS. We employ static analysis to track the flow of sensitive data and identify functions that handle such data. Permission-switching instructions are inserted at the function call and return points, adjusting permissions via the Granule Protection Table (GPT) to ensure that only designated functions can access the isolated data. Notably, CCAegis places trust solely in the Secure Monitor, which configures the GPTs and manages domain switching, thereby minimizing the TCB. We implemented CCAegis on both an official emulator and a real development board to assess its performance. Our experimental results show that CCAegis effectively isolates sensitive data and operations, with performance overheads ranging from 1.01× to 1.43× compared to the original version across real-world cryptographic workloads.

In this paper, we uncover a novel side-channel vulnerability arising from the shared NAT tables of Wi-Fi routers, enabling malicious insiders to hijack TCP connections between other clients and remote servers. First, by creating different NAT mappings within the shared NAT table, an off-path attacker can infer whether a victim client within the same Wi-Fi network is communicating with an external host over TCP, leveraging the widely adopted NAT port preservation strategy and insufficient reverse path validation in Wi-Fi routers. Once an active connection is detected, the attacker can manipulate the victim’s NAT mapping in the shared NAT table with spoofed TCP packets, exploiting the lack of TCP window tracking in most routers. In this way, the attacker can intercept TCP packets from the server and obtain the current sequence and acknowledgment numbers, which in turn allows the attacker to forcibly close the connection, poison the traffic in plain text, or reroute the server’s incoming packets to the attacker. We test 67 widely used routers from 30 vendors and discover that 52 of them are vulnerable. Also, we conduct an extensive measurement study on 93 real-world Wi-Fi networks and find that 75 of them (81%) are fully affected to our attack. Our case study shows that it takes about 17.5, 19.4, and 54.5 seconds on average to terminate SSH connections, download private files from FTP servers, and inject fake HTTP response packets with success rates of 87.4%, 82.6%, and 76.1%. Moreover, We evaluate the feasibility of the proposed attack in NAT-enabled IPv6 Wi-Fi networks. We responsibly disclose the vulnerability and suggest mitigation strategies to all affected vendors and have received positive feedback, including acknowledgments, CVEs, rewards, and adoption of our suggestions.

A timely software update is vital to combat the increasing security vulnerabilities. However, some software vendors may secretly patch their vulnerabilities without creating CVE entries or even describing the security issue in their change log. Thus, it is critical to identify these hidden security patches and defeat potential N-day attacks. Researchers have employed various machine learning techniques to identify security patches in open-source software, leveraging the syntax and semantic features of the software changes and commit messages. However, all these solutions cannot be directly applied to the binary code, whose instructions and program flow may dramatically vary due to different compilation configurations. In this paper, we propose BinGo, a new security patch detection system for binary code. The main idea is to present the binary code as code property graphs to enable a comprehensive understanding of program flow and perform a language model over each basic block of binary code to catch the instruction semantics. BinGo consists of four phases, namely, patch data pre-processing, graph extraction, embedding generation, and graph representation learning. Due to the lack of an existing binary security patch dataset, we construct such a dataset by compiling the pre-patch and post-patch source code of the Linux kernel. Our experimental results show BinGo can achieve up to 80.77% accuracy in identifying security patches between two neighboring versions of binary code. Moreover, BinGo can effectively reduce the false positives and false negatives caused by the different compilers and optimization levels.

Bluetooth Low Energy (BLE) is a short-range wireless communication technology for resource-constrained IoT devices. Unfortunately, BLE is vulnerable to session-based attacks, where previous packets construct exploitable conditions for subsequent packets to compromise connections. Defending against session-based attacks is challenging because each step in the attack sequence is legitimate when inspected individually. In this paper, we present BlueSWAT, a lightweight state-aware security framework for protecting BLE devices. To perform inspection on the session level rather than individual packets, BlueSWAT leverages a finite state machine (FSM) to monitor sequential actions of connections at runtime. Patterns of session-based attacks are modeled as malicious transition paths in the FSM. To overcome the heterogeneous IoT environment, we develop a lightweight eBPF framework to facilitate universal patch distribution across different BLE architectures and stacks, without requiring device reboot. We implement BlueSWAT on 5 real-world devices with different chips and stacks to demonstrate its cross-device adaptability. On our dataset with 101 real-world BLE vulnerabilities, BlueSWAT can mitigate 76.1% of session-based attacks, outperforming other defense frameworks. In our end-to-end application evaluation, BlueSWAT introduces an average of 0.073% memory overhead and negligible latency.

Generative artificial intelligence (AI) is versatile for various applications, but security and privacy concerns with third-party AI vendors hinder its broader adoption in sensitive scenarios. Hence, it is essential for users to validate the AI trustworthiness and ensure the security of data boundaries. In this paper, we present a dye testing system named Dye4AI, which injects crafted trigger data into human-AI dialogue and observes AI responses towards specific prompts to diagnose data flow in AI model evolution. Our dye testing procedure contains 3 stages: trigger generation, trigger insertion, and trigger retrieval. First, to retain both uniqueness and stealthiness, we design a new trigger that transforms a pseudo-random number to a intelligible format. Second, with a custom-designed three-step conversation strategy, we insert each trigger item into dialogue and confirm the model memorizes the new trigger knowledge in the current session. Finally, we routinely try to recover triggers with specific prompts in new sessions, as triggers can present in new sessions only if AI vendors leverage user data for model fine-tuning. Extensive experiments on six LLMs demonstrate our dye testing scheme is effective in ensuring the data boundary, even for models with various architectures and parameter sizes. Also, larger and premier models tend to be more suitable for Dye4AI, e.g., trigger can be retrieved in OpenLLaMa-13B even with only 2 insertions per trigger item. Moreover, we analyze the prompt selection in dye testing, providing insights for future testing systems on generative AI services.

Trained on billions of tokens, large language models (LLMs) have a broad range of empirical knowledge which enables them to generate software patches with complex repair patterns. We leverage the powerful code-fixing capabilities of LLMs and propose VarPatch, a multi-agent conversational automated program repair (APR) technique that iteratively queries the LLM to generate software patches by providing various prompts and context information. VarPatch focuses on the variable addition repair pattern, as previous APR tools struggle to introduce and use new variables to fix buggy code. Additionally, we summarize commonly used APIs and identify four repair patterns involving new variable addition. Our evaluation on the Defects4J 1.2 dataset shows that VarPatch can repair 69% more bugs than baseline tools and over 8 times more bugs than GPT-4.

Website Fingerprinting (WF) attacks can effectively identify the websites visited by Tor clients via analyzing encrypted traffic patterns. Existing attacks focus on identifying different websites, but their accuracy dramatically decreases when applied to identify fine-grained webpages, especially when distinguishing among different subpages of the same website. WebPage Fingerprinting (WPF) attacks face the challenges of highly similar traffic patterns and a much larger scale of webpages. Furthermore, clients often visit multiple webpages concurrently, increasing the difficulty of extracting the traffic patterns of each webpage from the obfuscated traffic. In this paper, we propose Oscar, a WPF attack based on multi-label metric learning that identifies different webpages from obfuscated traffic by transforming the feature space. Oscar can extract the subtle differences among various webpages, even those with similar traffic patterns. In particular, Oscar combines proxy-based and sample-based metric learning losses to extract webpage features from obfuscated traffic and identify multiple webpages. We prototype Oscar and evaluate its performance using traffic collected from 1,000 monitored webpages and over 9,000 unmonitored webpages in the real world. Oscar demonstrates an 88.6% improvement in the multi-label metric Recall@5 compared to the state-of-the-art attacks.

Deep learning-based classifiers have been widely used in the field of financial fraud transaction detection. However, training a high-performance classifier for fraud detection is challenging due to the lack of sufficient labeled fraud data. Particularly, it is difficult to detect stealthy fraud transactions that closely mimic benign user behaviors. We observe that the suspicious transactions identified by the online detection system can augment the feature space to improve the detection performance of machine learning-based models. In this paper, we propose a new framework GIANTESS to leverage suspicious transactions to augment the feature space and thus enhance the detection of stealthy fraud transactions. Our semi-supervised approach combines both labeled transactions and unlabeled suspicious transactions to train a detection model. Specifically, it first estimates pseudo labels of suspicious transactions and then combines the pseudo labels with ground truth labels to train the detection model. We conduct experiments on two real-world datasets to demonstrate the effectiveness of our proposed method on detecting stealthy fraud transactions. The experimental results show that GIANTESS successfully improves the recall by up to 6.3% at the fixed low false positive rate of 1%. We also perform a 9-week deployment test of our system in a real-world online payment platform to demonstrate the performance of GIANTESS.

While users tend to perceive instructional videos as an experience rather than a lesson with a set of instructions, instructional videos are more effective and appealing than textual user manuals and eliminate the ambiguity in text-based descriptions. However, most software vendors only offer document manuals that describe how to install and use their software, leading burden for non-professionals to comprehend the instructions. In this paper, we present a framework called M2V to generate instructional videos automatically based on the provided instructions and images in user manuals. M2V is a two-step framework. First, an action sequence is extracted from the given user manual via natural language processing and computer vision techniques. Second, M2V operates the software sequentially based on the extracted actions; meanwhile, the operation procedure is recorded into an instructional video. We evaluate the usability of automatically generated instructional videos via user studies and an online survey. The evaluation results show, with our toolkit, the generated instructional videos can better assist non-professional end users with the software operations. Moreover, more than 85% of survey participants prefer to use the instructional videos rather than the original user manuals.

ARM TrustZone technology has been widely used to create Trusted Execution Environments (TEEs) for enhancing the security of applications. However, the increasing number of installed security-sensitive applications in the secure world will inevitably enlarge the trusted computing base (TCB) of TEE systems. To minimize the TCB of the secure world and increase application portability, Isolated Execution Environments (IEEs) are proposed to protect applications in enclaves created in the normal world. However, existing IEE systems cannot provide the same level of security as the TEE systems, particularly, on resolving the multi-vector attacks that include both physical memory disclosure attacks and software attacks. In this article, we develop a new cache-assisted IEE system called CacheIEE that creates enclaves in the L1 data cache of the normal world to protect sensitive data against multi-vector attacks. First, by always storing the sensitive data in the L1 data cache, CacheIEE can effectively prevent physical memory disclosure attacks. Second, we protect the L1 data cache against untrusted rich OS running in other cores. To support more applications, CacheIEE can process large-size sensitive data in the L1 data cache with constrained capacity. We implement a system prototype of CacheIEE and verify its security and practicability.

Over-privileged Linux containers might put the underlying OS at risk by permitting pointless system calls that could be exploited as entry points to the kernel. However, finding such security profiles is a difficult task as it demands examining the implementation/operation of containers in the absence of knowledge regarding its required system calls. In this article, we propose a hybrid approach to limit the system call usage during the execution of containers. Specifically, given an application container, we maintain an initial fine-grained whitelist by dynamic tracking to control the run-time security along with a complementary whitelist extracted via static analysis to maintain container's functionality while addressing the coverage limitation of dynamic analysis. Our method automatically analyzes the container behavior to identify three execution phases and dynamically enforce the corresponding fine-grained system call whitelists. The invoked system call will be compared with both whitelists to decide if it should be killed to guarantee the container security or logged for further analysis. Our evaluation results with 193 Docker images demonstrate the effectiveness of our approach in significantly reducing the required system calls during the applications' life-cycle. Furthermore, we discuss the reduced attack surface and demonstrate the efficiency of our approach through empirical analysis results.

As the mainstream encrypted protocols adopt TCP protocol to ensure lossless data transmissions, the privacy of encrypted TCP traffic becomes a significant focus for adversaries. They can leverage Deep Learning (DL) models to infer the sensitive information from encrypted TCP traffic by analyzing its packet size, direction, and timing information. To defend against such DL-based traffic analysis attacks, recent advances reshape the encrypted traffic and achieve desired results. However, they typically require deploying cooperative modules on both communication endpoints and only support specific applications, such as browsers. In this paper, we propose Cactus, a client-side plug-in to obfuscate bidirectional encrypted TCP traffic for a wide range of applications transparently using the inherent TCP semantics and the emerging eBPF technique. In particular, Cactus provides four effective operations to enable bidirectional traffic obfuscation while preserving communication semantics of applications. Besides, Cactus empowers users to specify which applications to conduct traffic obfuscation and what obfuscation level for each application. We conduct comprehensive experiments to demonstrate that Cactus can effectively obfuscate encrypted TCP traffic with low overhead to hinder the traffic analysis efforts in website fingerprinting and application identification.

Container technology is widely adopted due to its features such as light weight and ease of rapid deployment. However, as an OS-level virtualization mechanism, container isolation relies on the kernel’s security mechanisms and the kernel permission data (usually non-control flow data) used by these mechanisms. None of the existing mitigation schemes for non-control flow data attacks provide an effective and practical solution to container security since they either trigger too much overhead, have limited effectiveness over attacks launched in specific ways, or can only be used to protect some specific kernel data. In addition, none of them accurately identify the kernel data associated with container isolation. In this paper, we provide a solution called Condo that enhances container isolation by protecting the associated kernel permission data. We first present a generic non-control flow kernel data protection mechanism that protects different types of kernel data uniformly with low overhead and is not limited by attack methods or data types. We then demystify the models of various kernel access control mechanisms in the container environment, and identify the subject and object permission data that are critical to container isolation. Finally, we provide a solution named Condo to enhance container isolation, which is completely transparent to the existing container ecosystem, including containerized applications and container management/orchestration tools such as Docker. Experimental results show that Condo can effectively reduce the compromises of container isolation due to memory corruption attacks with an acceptable overhead.

With the accelerating adaption of Cloud and Edge computing, cloud-based networked deployment emerges to enable providers to deliver services in a cost-effective and elastic manner. However, security concern remains one of the major obstacles to its wider adaption. Trusted Execution Environment (TEE) has been advocated to protect cloud services in an isolated execution environment. In this paper, we present a new genre of side-channel attack called interface-based side-channel attack and demonstrate its effectiveness on the TEE-assisted networked service system. The root cause of this attack is the input-dependent interface invocation (e.g., interface information and invocation patterns) that can be observed by untrusted software to reveal the control flows inside the enclave. Our evaluation demonstrates that the attack can effectively re-identify encrypted web pages processed in the SGX enclave with an accuracy of 87.6% and a recall of 76.6%, and can reduce the search domain of the 1024 bits RSA private keys to$1.69 \times 10^{-6}$of the original search domain. As countermeasures, we propose, implement and evaluate a set of static analysis tools to mitigate the newly discovered threats. The key idea is to use inter-procedural dataflow analysis to identify potential leakage via the interface, and then mitigate them during compilation using techniques including branch obfuscation, loop obfuscation, and constant size wrapper.

Python has become the most popular programming language as it is friendly to work with for beginners. However, a recent study has found that most security issues in Python have not been indexed by CVE and may only be fixed by "silent" security commits, which pose a threat to software security and hinder the security fixes to downstream software. It is critical to identify the hidden security commits; however, the existing datasets and methods are insufficient for security commit detection in Python, due to the limited data variety, non-comprehensive code semantics, and uninterpretable learned features. In this paper, we construct the first security commit dataset in Python, namely PySecDB, which consists of three subsets including a base dataset, a pilot dataset, and an augmented dataset. The base dataset contains the security commits associated with CVE records provided by MITRE. To increase the variety of security commits, we build the pilot dataset from GitHub by filtering keywords within the commit messages. Since not all commits provide commit messages, we further construct the augmented dataset by understanding the semantics of code changes. To build the augmented dataset, we propose a new graph representation named CommitCPG and a multi-attributed graph learning model named SCOPY to identify the security commit candidates through both sequential and structural code semantics. The evaluation shows our proposed algorithms can improve the data collection efficiency by up to 40 percentage points. After manual verification by three security experts, PySecDB consists of 1,258 security commits and 2,791 non-security commits. Furthermore, we conduct an extensive case study on PySecDB and discover four common security fix patterns that cover over 85% of security commits in Python, providing insight into secure software maintenance, vulnerability detection, and automated program repair.

Modern Wi-Fi networks are commonly protected by the security mechanisms, e.g., WPA, WPA2 or WPA3, and thus it is difficult for an attacker (a malicious supplicant) to hijack the traffic of other supplicants as a man-in-the-middle (MITM). In traditional Evil Twins attacks, attackers may deploy a bogus wireless access point (AP) to hijack the victim supplicants’ traffic (e.g., stealing credentials). In this paper, we uncover a new MITM attack that can evade the security mechanisms in Wi-Fi networks by spoofing the legitimate AP to send a forged ICMP redirect message to a victim supplicant and thus allow attackers to stealthily hijack the traffic from the victim supplicant without deploying any bogus AP. The core idea is to misuse the vulnerability of cross-layer interactions between WPAs and ICMP protocols, totally evading the link layer security mechanisms enforced by WPAs. We resolve two requirements to successfully launch our attack. First, when the attacker spoofs the legitimate AP to craft an ICMP redirect message, the legitimate AP cannot recognize and filter out those forged ICMP redirect messages. We uncover a new vulnerability (CVE-2022-25667) of the Network Processing Units (NPUs) in AP routers that restrict the AP routers from blocking fake ICMP error messages passing through the router. We test 55 popular wireless routers from 10 well-known AP vendors, and none of these routers can block the forged ICMP redirect messages due to this vulnerability. Second, we develop a new method to ensure the forged ICMP redirect message can evade the legitimacy check of the victim supplicant and then poison its routing table. We conduct an extensive measurement study on 122 real-world Wi-Fi networks, covering all prevalent Wi-Fi security modes. The experimental results show that 109 out of the 122 (89%) evaluated Wi-Fi networks are vulnerable to our attack. Besides notifying the vulnerability to the NPU manufacturers and the AP vendors, we develop two countermeasures to throttle the identified attack.

With the increasing popularity of open-source software, embedded vulnerabilities have been widely propagating to downstream software. Due to different maintenance policies, software vendors may silently release security patches without providing sufficient advisories (e.g., CVE). This leaves users unaware of security patches and provides attackers good chances to exploit unpatched vulnerabilities. Thus, detecting those silent security patches becomes imperative for secure software maintenance. In this paper, we propose a graph neural network based security patch detection system named GraphSPD, which represents patches as graphs with richer semantics and utilizes a patch-tailored graph model for detection. We first develop a novel graph structure called PatchCPG to represent software patches by merging two code property graphs (CPGs) for the pre-patch and post-patch source code as well as retaining the context, deleted, and added components for the patch. By applying a slicing technique, we retain the most relevant context and reduce the size of PatchCPG. Then, we develop the first end-to-end deep learning model called PatchGNN to determine if a patch is security-related directly from its graph-structured PatchCPG. PatchGNN includes a new embedding process to convert PatchCPG into a numeric format and a new multi-attributed graph convolution mechanism to adapt diverse relationships in PatchCPG. The experimental results show GraphSPD can significantly outperform the state-of-the-art approaches on security patch detection.

Android allows apps to communicate with its system services via system service helpers so that these apps can use various functions provided by the system services. Meanwhile, the system services rely on their service helpers to enforce security checks for protection. Unfortunately, the security checks in the service helpers may be bypassed via directly exploiting the non-SDK (hidden) APIs, degrading the stability and posing severe security threats such as privilege escalation, automatic function execution without users’ interactions, crashes, and DoS attacks. Google has proposed various approaches to address this problem, e.g., case-by-case fixing the bugs or even proposing a blacklist to block all the non-SDK APIs. However, the developers can still figure out new ways of exploiting these hidden APIs to evade the non-SDKs restrictions. In this article, we systematically study the vulnerabilities due to the hidden API exploitation and analyze the effectiveness of Google’s countermeasures. We aim to answer if there are still vulnerable hidden APIs that can be exploited in newest Android 12. We develop a static analysis tool called${{\sf ServiceAudit}}$to automatically mine the inconsistent security enforcement between service helper classes and the hidden service APIs. We apply${{\sf ServiceAudit}}$to Android 6$\sim$12. Our tool discovers 112 vulnerabilities in Android 6 with a higher precision than existing approaches. Moreover, in Android 11 and 12, we identify more than 25 hidden APIs with inconsistent protections; however, only one of the vulnerable APIs can lead to severe security problem in Android 11, and none of them work on Android 12.

The emerging Software-Defined Networking (SDN) is being adopted by data centers and cloud service providers to enable flexible control. Meanwhile, the current SDN design brings new vulnerabilities. In this paper, we explore a stealthy attack that uses a minimum rate of attack packets to disrupt SDN data plane. To achieve this, we propose the LOFT attack that computes the lower bound of attack rate to overflow flow tables based on the inferred network configurations. Particularly, each attack packet always triggers or maintains consumption of one flow rule. LOFT can ensure the attack effect under various network configurations while reducing the possibility of being captured. We demonstrate its feasibility and effectiveness in a real SDN testbed consisting of commercial hardware switches. The experimental results show that LOFT incurs significant network performance degradation and potential network DoS at an attack rate of only tens of Kbps. To defeat the attack, we develop a data-to-control plane collaborative defense system named LOFTGuard, which is lightweight and transparent to SDN applications. Evaluations show that LOFTGuard effectively protects SDN against the attack and introduces a small overhead.

Cloud gaming has become an emerging computing paradigm in recent years, allowing computer games to offload complex graphics and logic computation to the cloud. To deliver a smooth and high-quality gaming experience, cloud gaming services have invested abundant computing resources in the cloud, including adequate CPUs, top-tier GPUs, and high-bandwidth Internet connections. Unfortunately, the abundant computing resources offered by cloud gaming are vulnerable to misuse and exploitation for malicious purposes. In this paper, we present an in-depth study on security vulnerabilities in cloud gaming services. Specifically, we reveal that adversaries can purposely inject malicious programs/URLs into the cloud gaming services via game mods. Using the provided features such as in-game subroutines, game launch options, and built-in browsers, adversaries are able to execute the injected malicious programs/URLs in cloud gaming services. To demonstrate that such vulnerabilities pose a serious threat, we conduct four proof-of-concept attacks on cloud gaming services. Two of them are to abuse the CPUs and GPUs in cloud gaming services to mine cryptocurrencies with attractive profits and train machine learning models at a trivial cost. The other two are to exploit the high-bandwidth connections provided by cloud gaming for malicious Command & Control and censorship circumvention. Finally, we present several countermeasures for cloud gaming services to protect their valuable assets from malicious exploitation.

To bypass network censorship, Shadowsocks is often deployed on long-distance transnational networks; however, such proxy networks are usually plagued by high latency, high packet loss rate, and unstable bandwidth. Most existing tuning solutions rely on hand-tuned heuristics, which cannot work well in the volatile Shadowsocks networks due to the labor intensive and time-consuming properties. In this paper, we propose Auter, which automatically tunes multi-layer buffer parameters with reinforcement learning (RL) to improve the performance of Shadowsocks in long-distance networks. The key insight behind Auter is that different network environments require different sizes of buffers to achieve sufficiently good performance. Hence, Auter continuously learns a tuning policy from volatile network states and dynamically alter sizes of multi-buffers for high network performance. We prototype Auter and evaluate its effectiveness under various real networks. Our experimental results show that Auter can effectively improve network performance, up to 40.5% throughput increase in real networks. Besides, we demonstrate that Auter outperforms all the existing tuning schemes.

Provenance identification, which is essential for binary analysis, aims to uncover the specific compiler and configuration used for generating the executable. Traditionally, the existing solutions extract syntactic, structural, and semantic features from disassembled programs and employ machine learning techniques to identify the compilation provenance of binaries. However, their effectiveness heavily relies on disassembly tools (e.g., IDA Pro) and tedious feature engineering, since it is challenging to obtain accurate assembly code, particularly, from the stripped or obfuscated binaries. In addition, the features in machine learning approaches are manually selected based on the domain knowledge of one specific architecture, which cannot be applied to other architectures. In this paper, we develop an end-to-end provenance identification system BinProv, which leverages a BERT (Bidirectional Encoder Representations from Transformers) based embedding model to learn and represent the context semantics and syntax directly from the binary code. Therefore, BinProv avoids the disassembling step and manual feature selection in provenance identification. Moreover, BinProv can distinguish the compilers and the four optimization levels (O0/O1/O2/O3) by fine-tuning the classifier model with the embedding inputs for specific provenance identification tasks. Experimental results show that BinProv achieves 92.14%, 99.4%, and 99.8% accuracy at byte sequence, function, and binary levels, respectively. We further demonstrate that BinProv works well on obfuscated binary code, suggesting that BinProv is a viable approach to remarkably mitigate the disassembler dependence in future provenance identification tasks. Finally, our case studies show that BinProv can better identify compiler helper functions and improve the performance of binary code similarity detection.

As power and cooling cost has become a major factor in the total cost of ownership (TCO) of large-scale data centers, it is important to investigate how data centers run their cooling systems in practice. The data centers of Amazon Web Services (AWS) have been continuously expanding worldwide, and their restrictive security policies keep many management aspects of data centers private. In this paper, we make an attempt to explore the cooling systems of AWS data centers without privileged accesses. We first demonstrate PVT (process, voltage, and temperature) variations in AWS FPGAs (Field Programmable Gate Arrays) using time-digital converters (TDC). We further leverage the DRAM temperature side channel and improve the usage of the TDC to measure the temperature change accurately. We conduct a measurement on the daily temperatures of AWS data centers worldwide and find that temperature changes of some data centers are closely related to local weathers. Thus, we deduce they adopt free cooling techniques. This measurement study motivates us to re-think the vulnerability of data centers to power/thermal attacks.

Since the users of open source software (OSS) projects may not use the latest version all the time, OSS development teams often support code maintenance for old versions through maintaining multiple stable branches. Typically, the developers create a stable branch for each old stable version, deploy security patches on the branch, and release fixed versions at regular intervals. As such, old-version applications in production environments are protected from the disclosed vulnerabilities in a long time. However, the rapidly growing number of OSS vulnerabilities has greatly strained this patch deployment model, and a critical need has arisen for the security community to understand the practice of security patch management across stable branches. In this work, we conduct a large-scale empirical study of stable branches in OSS projects and the security patches deployed on them via investigating 608 stable branches belonging to 26 popular OSS projects as well as more than 2,000 security fixes for 806 CVEs deployed on stable branches.

System services and resources in Android are accessed through IPC-based mechanisms. Previous research has demonstrated that they are vulnerable to the denial-of-service attack (DoS attack). For instance, the JNI global reference (JGR), which is widely used by system services, can be exhausted to cause the system reboot (hence the name JGRE attack). Even though the Android team tries to fix the problem by enforcing security checks, we find that it is still possible to construct a JGR exhaustion DoS attack in the latest Android system. In this article, we propose a new JGR exhaustion DoS attack, which is effective in different Android versions, including thelatest one (i.e., Android 10). Specifically, we developed JGREAnalyzer, a tool that can systematically detect JGR vulnerable services APIs via a call graph analysis and a forwarding reachability analysis. We applied this tool to different Android versions and found multiple vulnerabilities. In particular, among 148 system services in Android 10, 12 of them have 21 vulnerabilities. Among them, 9 can be successfully exploited without any permissions. We further analyze the root cause of the vulnerabilities and propose a new defense to mitigate the JGRE attack by restricting resource consumption via global reference counting.

Account recovery (usually through a password reset) on many websites has mainly relied on accessibility to a registered email, due to its favorable deployability and usability. However, it makes a user's online accounts vulnerable to a single point of failure when the registered email account is compromised. While previous research focuses on strengthening user passwords, the security risk imposed by email-based password recovery has not yet been well studied. In this article, we first conduct a measurement study to characterize the password recovery activities in the wild. Specifically, we examine the authentication and password recovery protocols from 239 traffic-heavy websites, confirming that most of them use emails for password recovery. We further scrutinize the security policy of leading email service providers and show that a significant portion of them takes no or marginal effort to protect user email accounts, leaving compromised email accounts readily available for mounting password recovery attacks. Then, we conduct case studies to assess potential losses caused by such attacks. Finally, we propose and implement a lightweight email security enhancement called Secure Email Account Recovery (SEAR) to defend against password recovery attacks by adding an extra layer of protection to password recovery emails.

Reinforcement Learning (RL) has been applied to solve decision-making problems in computer network designs, especially in TCP congestion control. As RL-based congestion control methods enable powerful learning abilities, it achieves competitive performance and adaptiveness advantages over the traditional methods. However, RL-based systems suffer from adversarial attacks that generate perturbations to significantly degrade the performance. In this paper, we conduct a comprehensive study of adversarial attacks against RL-based congestion control systems. Unlike the state-of-the-art adversarial attacks on images where an attacker can easily obtain the input states to introduce perturbations, the attacker cannot directly obtain the input states in congestion control settings that are only available to the agents. It is challenging to add effective perturbations without knowing the input states for RL-based congestion control models. To solve the challenge, we develop an adversarial attack to estimate states of the target agent, craft adversarial perturbations, and apply the generated perturbations in an automated fashion. We evaluate how our adversarial attack affects the target agent’s decision-making process. Our experiments illustrate that our attack can effectively reduce about 50% average throughput while increasing more than 36x latency and 45% packet loss rate.

In this paper, we uncover a new off-path TCP hijacking attack that can be used to terminate victim TCP connections or inject forged data into victim TCP connections by manipulating the new mixed IPID assignment method, which is widely used in Linux kernel version 4.18 and beyond. Our attack has three steps. First, an off-path attacker can downgrade the IPID assignment for TCP packets from the more secure per-socket-based policy to the less secure hash-based policy, thus building a shared IPID counter that forms a side channel in the victim. Second, the attacker detects the presence of TCP connections by observing the side channel of the shared IPID counter. Third, the attacker infers sequence and acknowledgment numbers of the detected connection by observing the side channel. Consequently, the attacker can completely hijack the connection, e.g., resetting the connection or poisoning the data stream. We evaluate the impacts of our attack in the real world, and we uncover that more than 20% of Alexa top 100k websites are vulnerable to our attack. Our case studies of SSH DoS, manipulating web traffic, and poisoning BGP routing tables show its threat on a wide range of applications. Moreover, we demonstrate that our attack can be further extended to exploit IPv4/IPv6 dual-stack networks on increasing the hash collisions and enlarging vulnerable populations. Finally, we analyze the root cause and develop a new IPID assignment method to defeat this attack. We prototype our defense in Linux 4.18 and confirm its effectiveness in the real world.

Software-Defined Networking (SDN). SDN enables network innovations with a centralized controller controlling the whole network through the control channel. Because the control channel delivers all network control traffic, its security and reliability are of great importance. For the first time in the literature, we propose the CrossPath attack that disrupts the SDN control channel by exploiting the shared links in paths of control traffic and data traffic. In this attack, crafted data traffic can implicitly disrupt the forwarding of control traffic in the shared links. As the data traffic does not enter the control channel, the attack is stealthy and cannot be easily perceived by the controller. In order to identify the target paths containing the shared links to attack, we develop a novel technique called adversarial path reconnaissance. Our experimental results show its feasibility and efficiency of identifying the target path. We systematically study the impacts of the attack on various network applications in a real SDN testbed. Experiments show the attack significantly degrades the performance of existing network applications and causes serious network anomalies, e.g., routing blackhole, flow table resetting, and even network-wide DoS. To defeat the CrossPath attack, we design a lightweight defense system named CrossGuard. Experiments demonstrate that it can effectively protect the control channel and quickly locate the attack flow with 98% accuracy while introducing a small overhead.

A cloud-based data marketplace provides a service to match data shoppers with appropriate data sellers, so that data shoppers can augment their internal data sets with external data to improve their machine learning (ML) models. Since data may contain diverse values, it is critical for a shopper to evaluate the most valuable data before making the final trade. However, evaluating ML data typically requires the cloud to access a shopper’s ML model and sellers’ data, which are both sensitive. None of the existing cloud-based data marketplaces enable ML data evaluation while preserving both model privacy and data privacy. In this paper, we develop a privacy-preserving ML data evaluation framework on a cloud-based data marketplace to protect shoppers’ ML models and sellers’ data. First, we provide a privacy-preserving framework that allows shoppers and sellers to encrypt their models and data, respectively, while preserving data functionality and model functionality in the cloud. We then develop a privacy-preserving data selection protocol that enables the cloud to help shoppers select the most valuable ML data. Also, we develop a privacy-preserving data validation protocol that allows shoppers to further check the quality of the selected data. Compared to random data selection, the experimental results show that our solution can reduce 60% prediction errors.

Intel Software Guard Extensions (SGX) offers strong confidentiality and integrity protection to software programs running in untrusted operating systems. Unfortunately, SGX may be abused by attackers to shield suspicious payloads and conceal misbehaviors in SGX enclaves, which cannot be easily detected by existing defense solutions. There is no comprehensive study conducted to characterize malicious enclaves. In this paper, we present the first systematic study that scrutinizes all possible interaction interfaces between enclaves and the outside (i.e., cache-memory hierarchy, host virtual memory, and enclave-mode transitions), and identifies seven attack vectors. Moreover, we propose SGX-Bouncer, a detection framework that can detect these attacks by leveraging multifarious side-channel observations and SGX-specific features. We conduct empirical evaluations with existing malicious SGX applications, which suggests SGX-Bouncer can effectively detect various abnormal behaviors from malicious enclaves.

Graph Neural Networks (GNNs) have achieved state-of-the-art performance in various graph structure related tasks such as node classification and graph classification. However, GNNs are vulnerable to adversarial attacks. Existing works mainly focus on attacking GNNs for node classification; nevertheless, the attacks against GNNs for graph classification have not been well explored.

Security patches play an important role in defending against the security threats brought by the increasing OSS vulnerabilities. However, the collection of security patches still remains a challenging problem. Existing works mainly adopt a matching-based design that uses auxiliary information in CVE/NVD to reduce the search scope of patch commits. However, our preliminary study shows that these approaches can only cover a small part of disclosed OSS vulnerabilities (about 12%-53%) even with manual assistance.

Tracking user activities inside an enterprise network has been a fundamental building block for today's security infrastructure, as it provides accurate user profiling and helps security auditors to make informed decisions based on the derived insights from the abundant log data. Towards more accurate user tracking, we propose a novel paradigm named UTrack by leveraging rich system-level audit logs. From a holistic perspective, we bridge the semantic gap between user accounts and real users, tracking a real user's activities across different user accounts and different network hosts based on causal relationship among processes. To achieve better scalability and a more salient view, we apply a variety of data reduction and compression techniques to process the large amount of data. %and significantly reduce the data volume. We implement UTrack in a real enterprise environment consisting of 111 hosts, which generate more than 4 billion events in total during the experiment time of one month. Through our evaluation, we demonstrate that UTrack is able to accurately identify the events that are relevant to user activities. Our data reduction and compression modules largely reduce the output data size, producing a both accurate and salient overview on a user session profile.

Security patches, embedding both vulnerable code and the corresponding fixes, are of great significance to vulnerability detection and software maintenance. However, the existing patch datasets suffer from insufficient samples and low varieties. In this paper, we construct a large-scale patch dataset called PatchDB that consists of three components, namely, NVD-based dataset, wild-based dataset, and synthetic dataset. The NVD-based dataset is extracted from the patch hyperlinks indexed by the NVD. The wild-based dataset includes security patches that we collect from the commits on GitHub. To improve the efficiency of data collection and reduce the effort on manual verification, we develop a new nearest link search method to help find the most promising security patch candidates. Moreover, we provide a synthetic dataset that uses a new oversampling method to synthesize patches at the source code level by enriching the control flow variants of original patches. We conduct a set of studies to investigate the effectiveness of the proposed algorithms and evaluate the properties of the collected dataset. The experimental results show that PatchDB can help improve the performance of security patch identification.

Deep neural networks (DNNs) have been applied in a wide range of applications, e.g., face recognition and image classification; however, they are vulnerable to adversarial examples. By adding a small amount of imperceptible perturbations, an attacker can easily manipulate the outputs of a DNN. Particularly, the localized adversarial examples only perturb a small and contiguous region of the target object, so that they are robust and effective in both digital and physical worlds. Although the localized adversarial examples have more severe real-world impacts than traditional pixel attacks, they have not been well addressed in the literature. In this paper, we propose a generic defense system called TaintRadar to accurately detect localized adversarial examples via analyzing critical regions that have been manipulated by attackers. The main idea is that when removing critical regions from input images, the ranking changes of adversarial labels will be larger than those of benign labels. Compared with existing defense solutions, TaintRadar can effectively capture sophisticated localized partial attacks, e.g., the eye-glasses attack, while not requiring additional training or fine-tuning of the original model's structure. Comprehensive experiments have been conducted in both digital and physical worlds to verify the effectiveness and robustness of our defense.

Mobile devices are becoming an indispensable part of work for corporations and governments to store and process sensitive information. Thus, it is important for remote administrators to maintain control of these devices via Mobile Device Management (MDM) solutions. ARM TrustZone has been widely regarded as the de facto solution for protecting the security-sensitive software, such as MDM agents, from attacks of a compromised rich OS. However, little attention has been given to protecting the MDM control channel, a fundamental component for a remote administrator to invoke the TrustZone-based MDM agents and perform specific management operations. In this work, we design an ARM TrustZone-based network mechanism, called TZNIC, towards enabling resilient and secure access to TrustZone-based software, even in the presence of a malicious rich OS. TZNIC deploys two NIC drivers, one secure-world driver and one normal-world driver, multiplexing one physical NIC. We utilize the ARM TrustZone-based high privilege to protect the secure-world driver and further resolve several challenges on sharing one set of hardware peripherals between two isolated software environments. TZNIC does not require any changes or collaboration of the rich OS. We implement a prototype of TZNIC, and the evaluation results show that TZNIC can provide a reliable network channel to invoke the security software in the secure world, with minimal system overhead on the rich OS.

Before Android 5.0, the services in Android applications can be invoked either explicitly or implicitly. However, since the implicit service invocations may suffer service hijacking attacks and thus lead to sensitive data leakage, they have been forbidden since Android 5.0. Thereafter the Android system will simply throw an exception and crash the applications that still invokes services implicitly, so that it was expected that application developers will be forced to convert the implicit service invocations to explicit ones. In this paper, we develop a static analysis framework called ISA to analyze the effectiveness of forbidden policy on removing the vulnerable service invocations. We collect two datasets containing common 1390 apps downloaded 1 to 3 months before the forbidden policy is enforced and 30 months after the forbidden policy is enforced, respectively. Our preliminary analysis indicates a 82.58% reduction in the number of vulnerable service invocations due to the enforcement of forbidden policy. However, upon further investigation, we discover that the forbidden policy fails to resolve service hijacking attacks. We find that 36 popular applications are still vulnerable to service hijacking attacks, which can lead to the leakage of sensitive information such as user login credential. Finally, we analyze the reasons of the residue vulnerable invocations and then propose two countermeasures.

Searchable symmetric encryption (SSE) enables users to search over encrypted documents in untrusted clouds without leaking the search keywords to the clouds. Existing SSE schemes achieve high search efficiency at the expense of leaking access patterns and search patterns, where clouds can recover a large percentage of queried keywords using the leaked access patterns and search patterns. To prevent clouds from recovering users' keywords, researchers have proposed a number of solutions to protect either search patterns or access patterns. However, none of them can protect both access patterns and search patterns. Moreover, existing SSE schemes cannot work in the generic database setting that allows multiple users to write or read over encrypted documents. In this paper, we propose an efficient searchable symmetric encryption scheme, called SAP-SSE, which protects both access patterns and search patterns in the generic database setting. The main idea of protecting search patterns is to leverage re-encryption cryptosystems to shuffle index entries over multiple clouds. To protect access patterns, we distribute secure indexes to multiple clouds and then propose an index redistribution protocol that allows users to renew index entries in clouds. Furthermore, SAP-SSE provides a configurable security policy to balance security and efficiency. Formal security analysis and experimental evaluation show that SAP-SSE can prevent pattern leakage with low overhead.

In the past decade, Trusted Execution Environment (TEE) provided by ARM TrustZone is becoming one of the primary techniques for enhancing the security of mobile devices. The isolation enforced by TrustZone can protect the trusted applications running in the TEE against malicious software in the untrusted rich execution environment (REE). However, TrustZone cannot completely prevent vulnerabilities in trusted applications residing in the TEE, which can then be used to attack other trusted applications or even the trusted OS. Previously, a number of memory corruption vulnerabilities have been reported on different TAs, which are written in memory-unsafe languages like C.

In this paper, we uncover a new off-path TCP hijacking attack that can be used to terminate victim TCP connections or inject forged data into victim TCP connections by manipulating the new mixed IPID assignment method, which is widely used in Linux kernel version 4.18 and beyond to help defend against TCP hijacking attacks. The attack has three steps. First, an off-path attacker can downgrade the IPID assignment for TCP packets from the more secure per-socket-based policy to the less secure hash-based policy, building a shared IPID counter that forms a side channel on the victim. Second, the attacker detects the presence of TCP connections by observing the shared IPID counter on the victim. Third, the attacker infers the sequence number and the acknowledgment number of the detected connection by observing the side channel of the shared IPID counter. Consequently, the attacker can completely hijack the connection, i.e., resetting the connection or poisoning the data stream. We evaluate the impacts of this off-path TCP attack in the real world. Our case studies of SSH DoS, manipulating web traffic, and poisoning BGP routing tables show its threat on a wide range of applications. Our experimental results show that our off-path TCP attack can be constructed within 215 seconds and the success rate is over 88%. Finally, we analyze the root cause of the exploit and develop a new IPID assignment method to defeat this attack. We prototype our defense in Linux 4.18 and confirm its effectiveness through extensive evaluation over real applications on the Internet.

The traditional usage of ARM TrustZone has difficulty on solving the conflicts between the manufacturers that want to minimize the trusted computing base by constraining the installation of third-party applications in the secure world and the third-party application developers who prefer to have the freedom of installing their applications into the secure world. To address this issue, researchers propose to create Isolated Execution Environments (called IEEs) in the normal world to protect the security-sensitive applications. In this paper, we perform a systematic study on the IEE data protection models and the ARM cache attributes, and discover three cache-based attacks called CITM that can be leveraged to manipulate the sensitive data protected in IEEs. Specifically, due to the inefficient and incoherent security measures on the cache that maps to the IEE memory (i.e., memory designated for IEEs), attackers in the normal world may compromise the security of IEE data by manipulating the IEE memory during concurrent execution, bypassing the security measures enforced when a security-sensitive application is suspended or finished, or misusing the incomplete security measures during IEE's context switching processes. We conduct case studies of CITM attacks on three well-known IEE systems including SANCTUARY, Ginseng, and TrustICE to illustrate the feasibility to exploit them on real hardware testbeds. Finally, we analyze the root causes of the CITM attacks and propose a countermeasure to defeat them. The experimental results show that our defense scheme has a small overhead.

Automatic speech recognition (ASR) systems have been widely deployed in modern smart devices to provide convenient and diverse voice-controlled services. Since ASR systems are vulnerable to audio replay attacks that can spoof and mislead ASR systems, a number of defense systems have been proposed to identify replayed audio signals based on the speakers' unique acoustic features in the frequency domain. In this paper, we uncover a new type of replay attack called modulated replay attack, which can bypass the existing frequency domain based defense systems. The basic idea is to compensate for the frequency distortion of a given electronic speaker using an inverse filter that is customized to the speaker's transform characteristics. Our experiments on real smart devices confirm the modulated replay attacks can successfully escape the existing detection mechanisms that rely on identifying suspicious features in the frequency domain. To defeat modulated replay attacks, we design and implement a countermeasure named DualGuard. We discover and formally prove that no matter how the replay audio signals could be modulated, the replay attacks will either leave ringing artifacts in the time domain or cause spectrum distortion in the frequency domain. Therefore, by jointly checking suspicious features in both frequency and time domains, DualGuard~can successfully detect various replay attacks including the modulated replay attacks. We implement a prototype of DualGuard~on a popular voice interactive platform, ReSpeaker Core v2. The experimental results show DualGuard~can achieve 98% accuracy on detecting modulated replay attacks.

The Trusted Execution Environment (TEE) has been widely used to protect the security-sensitive sensing systems on Internet-of-Thing (IoT) devices. In the TEE systems, the execution environment is securely divided into a normal domain and a higher privileged secure domain which executing sensing systems through hardware. One common way to achieve the protection is implementing the sensitive functions of the sensing systems as trusted applications (TAs) in the well-isolated secure domain. Users in rich OS have to call TAs through the client applications (CAs), and the invocations must pass through the rich OS kernel. However, an untrusted rich OS may launch man-in-the-middle attacks on the communication between the CAs and TAs, and the misuse of cross-domain communication channel is becoming one severe threat on the TEE systems. In this paper, we develop a defense system named TrustICT to construct a lightweight trusted interaction channel between CAs and TAs without modifying existing TEE architecture. The main idea is to block attacks on the cross-domain interactions via dynamically setting the access permission of domain-shared memory, locking it from kernel mode and unlocking it only to legal CAs in the user mode. Particularly, we propose a multi-core scheduling strategy to defeat potential attacks from all privileged cores. Compared to existing cryptography-based methods, TrustICT dramatically reduces the system overhead since it does not require time-consuming cryptographic computation or sophisticated real-time kernel protection. We implement a prototype of TrustICT on a Freescale i.MX6Quad platform with the OP-TEE software system and evaluate its impacts on rich OS and the cross-domain transactions.

On ARM processors with TrustZone security extension, asynchronous introspection mechanisms have been developed in the secure world to detect security policy violations in the normal world. These mechanisms provide security protection via passively checking the normal world snapshot. However, since previous secure world checking solutions require to suspend the entire rich OS, asynchronous introspection has not been widely adopted in the real world. Given a multi-core ARM system that can execute the two worlds simultaneously on different cores, secure world introspection can check the rich OS without suspension. However, we identify a new normal-world evasion attack that can defeat the asynchronous introspection by removing the attacking traces in parallel from one core when the security checking is performing on another core. We perform a systematic study on this attack and present its efficiency against existing asynchronous introspection mechanisms. As the countermeasure, we propose a secure and trustworthy asynchronous introspection mechanism called SATIN, which can efficiently detect the evasion attacks by increasing the attackers' evasion time cost and decreasing the defender's execution time under a safe limit. We implement a prototype on an ARM development board and the experimental results show that SATIN can effectively prevent evasion attacks on multi-core systems with a minor system overhead.

Security patches in open source software (OSS) not only provide security fixes to identified vulnerabilities, but also make the vulnerable code public to the attackers. Therefore, armored attackers may misuse this information to launch N-day attacks on unpatched OSS versions. The best practice for preventing this type of N-day attacks is to keep upgrading the software to the latest version in no time. However, due to the concerns on reputation and easy software development management, software vendors may choose to secretly patch their vulnerabilities in a new version without reporting them to CVE or even providing any explicit description in their change logs. When those secretly patched vulnerabilities are being identified by armored attackers, they can be turned into powerful "0-day" attacks, which can be exploited to compromise not only unpatched version of the same software, but also similar types of OSS (e.g., SSL libraries) that may contain the same vulnerability due to code clone or similar design/implementation logic. Therefore, it is critical to identify secret security patches and downgrade the risk of those "0-day" attacks to at least "n-day" attacks. In this paper, we develop a defense system and implement a toolset to automatically identify secret security patches in open source software. To distinguish security patches from other patches, we first build a security patch database that contains more than 4700 security patches mapping to the records in CVE list. Next, we identify a set of features to help distinguish security patches from non-security ones using machine learning approaches. Finally, we use code clone identification mechanisms to discover similar patches or vulnerabilities in similar types of OSS. The experimental results show our approach can achieve good detection performance. A case study on OpenSSL, LibreSSL, and BoringSSL discovers 12 secret security patches.

Web crawlers have been misused for several malicious purposes such as downloading server data without permission from the website administrator. Moreover, armoured crawlers are evolving against new anti-crawler mechanisms in the arm races between crawler developers and crawler defenders. In this paper, based on one observation that normal users and malicious crawlers have different short-term and long-term download behaviours, we develop a new anti-crawler mechanism called PathMarker to detect and constrain persistent distributed crawlers. By adding a marker to each Uniform Resource Locator (URL), we can trace the page that leads to the access of this URL and the user identity who accesses this URL. With this supporting information, we can not only perform more accurate heuristic detection using the path related features, but also develop a Support Vector Machine based machine learning detection model to distinguish malicious crawlers from normal users via inspecting their different patterns of URL visiting paths and URL visiting timings. In addition to effectively detecting crawlers at the earliest stage, PathMarker can dramatically suppress the scraping efficiency of crawlers before they are detected. We deploy our approach on an online forum website, and the evaluation results show that PathMarker can quickly capture all 6 open-source and in-house crawlers, plus two external crawlers (i.e., Googlebots and Yahoo Slurp).

Linux container mechanism has attracted a lot of attention and is increasingly utilized to deploy industry applications. Though it is a consensus that the container mechanism is not secure due to the kernel-sharing property, it lacks a concrete and systematical evaluation on its security using real world exploits. In this paper, we collect an attack dataset including 223 exploits that are effective on the container platform, and classify them into different categories using a two-dimensional attack taxonomy. Then we evaluate the security of existing Linux container mechanism using 88 typical exploits filtered out from the dataset. We find 50 (56.82%) exploits can successfully launch attacks from inside the container with the default configuration. Since the privilege escalation exploits can completely disable the container protection mechanism, we conduct an in-depth analysis on these exploits. We find the kernel security mechanisms such as Capability, Seccomp, and MAC play a more important role in preventing privilege escalation than the container isolation mechanisms (i.e., Namespace and Cgroup). However, the interdependence and mutual-influence relationship among these kernel security mechanisms may make them fall into the "short board effect" and impair their protection capability. By studying the 11 exploits that still can successfully break the isolation provided by container and achieve privilege escalation, we identify a common 4-step attack model followed by all 11 exploits. Finally, we propose a defense mechanism to effectively defeat those identified privilege escalation attacks.

Account recovery (usually through a password reset) on many websites has mainly relied on accessibility to a registered email due to its favorable deployability and usability. However, it makes a user's online accounts vulnerable to a single point of failure when the registered email account is compromised. While previous research focuses on strengthening user passwords, the security risk imposed by email-based account recovery has not yet been well studied. In this paper, we investigate the possibility of mounting an email-based account recovery attack. Specifically, we examine the account authentication and recovery protocols in 239 traffic-heavy websites, confirming that most of them use emails for account recovery. We further scrutinize the security policy of major email service providers and show that a significant portion of them take no or marginal effort to protect user email accounts, leaving compromised email accounts readily available for mounting account recovery attacks. Then, we conduct case studies to assess potential losses caused by such attacks. Finally, we propose a lightweight email security enhancement called Secure Email Account Recovery (SEAR) to defend against account recovery attacks as an extra layer of protection to account recovery emails.

With the emergence of Internet of Things, mobile devices are generating more network traffic than ever. TrustZone is a hardware-enabled trusted execution environment for ARM processors. While TrustZone is effective in providing the much-needed memory isolation, we observe that it is possible to derive secret information from secure world using the cache contention, due to its high-performance cache sharing design. In this work, we propose TruSense to study the timing-based cache side-channel information leakage of TrustZone. TruSense can be launched from not only the normal world operating system but also a non-privileged user application. Without access to virtual-to-physical address mapping in user applications, we devise a novel method that uses the expected channel statistics to allocate memory for cache probing. We also show how an attacker might use the less accurate performance event interface as a timer. Using the T-table based AES implementation in OpenSSL 1.0.1f as an example, we demonstrate how a normal world attacker can steal fine-grained secret in the secure world. We also discuss possible mitigations for the information leakage.

With increasingly complex cyber attacks occurring every day, memory-based forensic techniques are becoming instrumental in digital investigations. Forensic examiners can unravel what happened on a system by acquiring and inspecting in-memory data. However, the foundation of this analysis can be invalidated if the memory acquisition has been altered. In this paper, we study the feasibility of malicious software misusing architectural features to sabotage memory forensics. The misuse of two architectural features, namely, physical address layout and secure containers, is presented. The first architectural feature explored in this paper is the physical address layout. It is used by the northbridge to route memory access to either physical memory or I/O devices on x86 platforms. Observing this design choice, we propose Hidden in I/O Space (HIveS), which manipulates CPU registers to alter the physical address layout to conceal memory. The system uses a novel I/O shadowing technique to lock a memory region named HIveS memory into I/O address space to prevent access. Two novel techniques, blackbox write and TLB camouflage, are developed to further protect the unlocked HIveS memory against memory forensics while allowing access for attackers. The second architectural feature explored in this paper is hardware-aided secure execution technology. More specifically, hardware-enforced memory encryption in Intel secure guard extension is used in malicious enclave software (Malclaveware) to prevent introspection and memory forensics. A prototype of HIveS is built and tested against a set of memory acquisition tools for both Windows and Linux running on the x86 platform. Malclaveware is also prototyped in Windows to demonstrate the risk. More importantly, we proposed countermeasures and mitigations for the newly discovered attacks. Through these discussions, we aim to raise the awareness of the potential risks of misusing hardware architectural features.

The services in Android applications can be invoked either explicitly or implicitly before Android 5.0. However, since the implicit service invocations suffer service hijacking attacks and thus lead to sensitive information leakage, they have been forbidden since Android 5.0. Thereafter since the Android system will simply throw an exception and crash the application that still invokes services implicitly, it was expected that application developers will be forced to convert the implicit service invocations to explicit ones by specifying the package name of the service to be called.

Android system applies a permission-based security model to restrict unauthorized apps from accessing system services, however, this security model cannot constrain authorized apps from sending excessive service requests to exhaust the limited system resource allocated for each system service. As references from native code to a Java object, JNI Global References (JGR) are prone to memory leaks, since they are not automatically garbage collected. Moreover, JGR exhaustion may lead to process abort or even Android system reboot when the victim process could not afford the JGR requests triggered by malicious apps through inter-process communication. In this paper, we perform a systematic study on JGR exhaustion (JGRE) attacks against all system services in Android. Our experimental results show that among the 104 system services in Android 6.0.1, 32 system services have 54 vulnerabilities. Particularly, 22 system services can be successfully attacked without any permission support. After reporting those vulnerabilities to Android security team and getting confirmed, we study the existing ad hoc countermeasures in Android against JGRE attacks. Surprisingly, among the 10 system services that have been protected, 8 system services are still vulnerable to JGRE attacks. Finally, we develop an effective defense mechanism to defeat all identified JGRE attacks by adopting Android's low memory killer (LMK) mechanism.

User preference plays an important factor in E-commerce websites for advertising and marketing, and the disclosure of user preference could also raise privacy concerns. As one of the largest E-commerce platform, Amazon features a wishlist that allows users to keep track of their desired products. In this paper, we investigate Amazon wishlist, and its possible privacy exposure. To this end, we collect complete wishlists of over 30,000 users, by analyzing which we are able to make interesting observations based on user online shopping preference in multiple dimensions. Specifically, we show user preference variation from different demographical groups, including gender and geo-locations. Taking timing factors into consideration, we also observe that unlike traditional walk-in-shop type of shopping, there is no significant difference in the dynamics of Amazon wishlists between weekdays and weekend. In the investigation of user information exposure in Amazon wishlists, we parse and analyze list-descriptions, illustrating which and to what extent user personal information is exposed to the public. Finally, we demonstrate that the information in wishlists has potential to leak a user's private personal information. Based on the collected user data, we can predict user gender with over 80% accuracy by just exploiting items present in Amazon wishlists.

Web crawlers have been misused for several malicious purposes such as downloading server data without permission from the website administrator. In this paper, based on one observation that normal users and malicious crawlers have different short-term and long-term download behaviors, we develop a new anti-crawler mechanism called PathMarker to detect and constrain persistent distributed crawlers. For each URL, by adding a marker to record its parent page that leads to the access to this URL and the user identity who accesses this URL, we can not only perform more accurate heuristic detection and Support Vector Machine (SVM) based machine learning detection to detect malicious crawlers at an earlier stage, but also dramatically suppress the efficiency of crawlers before they are detected. We deploy our approach on a forum website, and the evaluation results show that PathMarker can quickly capture all 6 open-source and in-house crawlers.

In this paper, we propose a Cookie-based Amplification Repression Protocol (CARP) to address the increasing threat of amplification attack. As a replacement of UDP protocol, CARP outperforms previous works in three aspects: i) CARP is a generic solution for all UDP-based amplification attacks regardless of the application protocols; ii) CARP incurs low-latency and introduces no additional latency in most use cases which is suitable for all UDP-based application scenario; iii) CARP supports incremental deployment and plug-and-play. Its interest-driven deployment model makes it much easier to be adopted. We implement a prototype of CARP and evaluate its performance on different metrics. Our results show that CARP is a lightweight and efficient solution for mitigating amplification attack.

While it is not recommended, Internet users tend to include personal information in their passwords for easy memorization. However, the use of personal information in passwords and its security implications have yet to be studied. In this paper, we dissect user passwords from several leaked data sets to investigate the extent to which a user's personal information resides in a password. Then, we introduce a new metric called coverage to quantify the correlation between passwords and personal information. Afterward, based on our analysis, we extend the probabilistic context-free grammars (PCFGs) method to be semantics-rich and propose personal-PCFG to crack passwords by generating personalized guesses. Through offline and online attack scenarios, we demonstrate that personal-PCFG cracks passwords much faster than PCFG and makes online attacks much more likely to succeed. To defend against such semantics-aware attacks, we examine the use of simple distortion functions that are chosen by users to mitigate unwanted correlation between personal information and passwords.

The postMessage mechanism in HTML5 enables different webpage origins to exchange information and communicate. It becomes increasingly popular among the websites that need to import contents from third-party services, such as advertisements and preferable recommendations. Ideally, a receiver function should be locally implemented in the hosting page that needs to receive third-party messages. However, in the real world, the receiver function is usually provided by a third-party service provider, and the function code is imported via the HTML "script" tag so that the imported code is deemed as from the same origin with the hosting page. In the case that a site uses multiple third-party services, all the receiver functions imported by the hosting page can receive messages from any third-party provider. Based on this observation, we identify a new information leakage threat named DangerNeighbor attacks that allow a malicious service eavesdrop messages from other services to the hosting page.

With the growing importance of networked embedded devices in the upcoming Internet of Things, new attacks targeting embedded OSes are emerging. ARM processors, which power over 60% of embedded devices, introduce a hardware security extension called TrustZone to protect secure applications in an isolated secure world that cannot be manipulated by a compromised OS in the normal world. LeveragingTrustZone technology, a number of memory integrity checking schemes have been proposed in the secure world to introspect malicious memory modification of the normal world. In this paper, we first discover and verify an ARM TrustZone cache incoherence behavior, which results in the cache contents of the two worlds, secure and non-secure, potentially being different even when they are mapped to the same physical address. Furthermore, code in one TrustZone world cannot access the cache content in the other world. Based on this observation, we develop a new rootkit called CacheKit that hides in the cache of the normal world and is able to evade memory introspection from the secure world. We implement a CacheKit prototype on Cortex-A8 processors after solving a number of challenges. First, we employ the Cache-as-RAM technique to ensure that the malicious code is only loaded into the CPU cache and not RAM. Thus, the secure world cannot detect the existence of the malicious code by examining the RAM. Second, we use the ARM processor's hardware support on cache settings to keep the malicious code persistent in the cache. Third, to evade introspection that flushes cache content back into RAM, we utilize physical addresses from the I/O address range that is not backed by any real I/O devices or RAM. The experimental results show that CacheKit can successfully evade memory introspection from the secure world and has small performance impacts on the rich OS. We discuss potential countermeasures to detect this type of rootkit attack.

While numerous flaws have been recognized in using passwords as a method of authentication, passwords still remain the de-facto authentication standard in use today. Though password managers can ameliorate password fatigue, the vast majority of password managers require the user to choose and maintain a strong master password while offering little to no recourse in the event that the master password is compromised. The wide-application of cloud-based password managers congregate passwords in an encrypted database, which becomes an attractive target for attackers and also represents a single point of failure. In this paper, we propose Amnesia, a bilateral generative password manager that requires both the knowledge of the master password and the possession of the user's smartphone to generate website passwords for the user. Our generative password manager is not vulnerable to the password database leakage, since it generates the requested password on demand using both the master password and the secret information on the smartphone. An attacker wishing to steal the user's website passwords has to compromise both the user's smartphone and the master password. Amnesia also has strong recovery capability when either the master password is compromised or the smartphone is lost/stolen. By using an Amnesia server, a user can have the access to the password manager on multiple computers without installing any software on those computers. We implemented an Amnesia system prototype using Android and Cherrypy web framework and evaluated it in terms of security, usability, and overhead. A user study of 31 testers shows that Amnesia increases password security while maintaining reasonable user convenience.

Though not recommended, Internet users often include parts of personal information in their passwords for easy memorization. However, the use of personal information in passwords and its security implications have not yet been studied systematically in the past. In this paper, we first dissect user passwords from a leaked dataset to investigate how and to what extent user personal information resides in a password. In particular, we extract the most popular password structures expressed by personal information and show the usage of personal information. Then we introduce a new metric called Coverage to quantify the correlation between passwords and personal information. Afterwards, based on our analysis, we extend the Probabilistic Context-Free Grammars (PCFG) method to be semantics-rich and propose Personal-PCFG to crack passwords by generating personalized guesses. Through offline and online attack scenarios, we demonstrate that Personal-PCFG cracks passwords much faster than PCFG and makes online attacks much easier to succeed.

Sophisticated adversaries usually initiate their attacks with a reconnaissance phase to discover exploitable vulnerabilities on the targeted networks and systems. To mitigate the effectiveness of persistent reconnaissance attacks, we develop a defensive mechanism that dynamically mutates network topology with a large number of decoys to invalidate the attacker's knowledge from network scanning. We combine the IP randomization technique with decoy techniques and solve two challenges, namely, service availability to legitimate users and service security against unauthorized users. First, our solution can minimize the probability of the real servers being identified and compromised by unauthorized users through deploying a large number of decoy nodes, which change their IP addresses along with the real servers to prolong the scanning time of the attackers. Second, our solution can ensure seamless connection migration so that all existing communication connections between the legitimate users and the servers are always kept alive even after the servers migrate to different IP addresses multiple times. We implement a virtual machine based system prototype and evaluate it using state-of-the-art scanning techniques. Both theoretical analysis and experimental results show that our solution can effectively mitigate network reconnaissance attacks without sacrificing service availability.

Recognizing the pressing demands to secure embedded applications, ARM TrustZone has been adopted in both academic research and commercial products to protect sensitive code and data in a privileged, isolated execution environment. However, the design of TrustZone cannot prevent physical memory disclosure attacks such as cold boot attack from gaining unrestricted read access to the sensitive contents in the dynamic random access memory (DRAM). A number of system-on-chip (SoC) bound execution solutions have been proposed to thaw the cold boot attack by storing sensitive data only in CPU registers, CPU cache or internal RAM. However, when the operating system, which is responsible for creating and maintaining the SoC-bound execution environment, is compromised, all the sensitive data is leaked. In this paper, we present the design and development of a cache-assisted secure execution framework, called CaSE, on ARM processors to defend against sophisticated attackers who can launch multi-vector attacks including software attacks and hardware memory disclosure attacks. CaSE utilizes TrustZone and Cache-as-RAM technique to create a cache-based isolated execution environment, which can protect both code and data of security-sensitive applications against the compromised OS and the cold boot attack. To protect the sensitive code and data against cold boot attack, applications are encrypted in memory and decrypted only within the processor for execution. The memory separation and the cache separation provided by TrustZone are used to protect the cached applications against compromised OS. We implement a prototype of CaSE on the i.MX53 running ARM Cortex-A8 processor. The experimental results show that CaSE incurs small impacts on system performance when executing cryptographic algorithms including AES, RSA, and SHA1.

Two-factor authentication has been widely used due to the vulnerabilities associated with traditional text-based password. One-time password (OTP) plays an indispensable role on authenticating mobile users to critical web services that demand a high level of security. As the smartphones are increasingly gaining popularity nowadays, software-based OTP generators have been developed and installed into smartphones as software apps, which bring great convenience to the users without introducing extra burden. However, software-based OTP solutions cannot guarantee the confidentiality of the generated passwords or even the seeds when the mobile OS is compromised. Moreover, they also suffer from denial-of-service attacks when the mobile OS crashes. Hardware-based OTP tokens can solve these security problems in the software-based OTP solutions; however, it is inconvenient for the users to carry physical tokens with them, particularly, when there are more than one token to be carried. In this paper, we present TrustOTP, a secure one-time password solution that can achieve both the flexibility of software tokens and the security of hardware tokens by using ARM TrustZone technology. TrustOTP can not only protect the confidentiality of the OTPs against a malicious mobile OS, but also guarantee reliable OTP generation and trusted OTP display when the mobile OS is compromised or even crashes. It is flexible to integrate multiple OTP algorithms and instances for different application scenarios on the same smartphone platform without modifying the mobile OS. We develop a prototype of TrustOTP on Freescale i.MX53 QSB. The experimental results show that TrustOTP has small impacts on the mobile OS and its power consumption is low.

With the growing complexity of computing systems, memory based forensic techniques are becoming instrumental in digital investigations. Digital forensic examiners can unravel what happened on a system by acquiring and inspecting in-memory data. Meanwhile, attackers have developed numerous anti-forensic mechanisms to defeat existing memory forensic techniques by manipulation of system software such as OS kernel. To counter anti-forensic techniques, some recent researches suggest that memory acquisition process can be trusted if the acquisition module has not been tampered with and all the operations are performed without relying on any untrusted software including the operating system.

Mobile devices have been widely used to process sensitive data and perform important transactions. It is a challenge to protect secure code from a malicious mobile OS. ARM TrustZone technology can protect secure code in a secure domain from an untrusted normal domain. However, since the attack surface of the secure domain will increase along with the size of secure code, it becomes arduous to negotiate with OEMs to get new secure code installed. We propose a novel TrustZone-based isolation framework named TrustICE to create isolated computing environments (ICEs) in the normal domain. TrustICE securely isolates the secure code in an ICE from an untrusted Rich OS in the normal domain. The trusted computing base (TCB) of TrustICE remains small and unchanged regardless of the amount of secure code being protected. Our prototype shows that the switching time between an ICE and the Rich OS is less than 12 ms.

With the rapid proliferation of malware attacks on the Internet, understanding these malicious behaviors plays a critical role in crafting effective defense. Advanced malware analysis relies on virtualization or emulation technology to run samples in a confined environment, and to analyze malicious activities by instrumenting code execution. However, virtual machines and emulators inevitably create artifacts in the execution environment, making these approaches vulnerable to detection or subversion. In this paper, we present MALT, a debugging framework that employs System Management Mode, a CPU mode in the x86 architecture, to transparently study armored malware. MALT does not depend on virtualization or emulation and thus is immune to threats targeting such environments. Our approach reduces the attack surface at the software level, and advances state-of-the-art debugging transparency. MALT embodies various debugging functions, including register/memory accesses, breakpoints, and four stepping modes. We implemented a prototype of MALT on two physical machines, and we conducted experiments by testing an array of existing anti-virtualization, anti-emulation, and packing techniques against MALT. The experimental results show that our prototype remains transparent and undetected against the samples. Furthermore, our prototype of MALT introduces moderate but manageable overheads on both Windows and Linux platforms.

It is well-known that Android mobile advertising networks may abuse their host applications' permission to collect private information. Since the advertising library and host app are running in the same process, the current Android permission mechanism cannot prevent an ad network from collecting private data that is out of an ad network's permission range. In this paper, we propose PmDroid to protect the data that is not under the scope of the ad network's permission set. PmDroid can block the data from being sent to advertising servers at the occurrence of permission violation in ad networks. Moreover, we utilize PmDroid to assess how serious the permission violation problem is in the ad networks. We first implement 53 sample apps using a single ad network library. We grant all permissions of Android 4.3 to these apps and record the data sent to the Internet. Then, we further analyze 430 published market apps. In total, there are 76 ad networks identified in our experiments. We compare the permission of data received by these ad networks with their official documents. Our experimental results indicate that the permission violation is a real problem in existing ad network markets.

With the wide usage of smartphones in our daily life, new malware is emerging to compromise the mobile OS and then steal or manipulate sensitive data from mobile applications. Forensic analysis tools demand a reliable and trustworthy memory acquisition of the operating systems running on the smartphones for further digital forensic analysis. However, a compromised OS may launch denial of service attacks to prevent a valid memory acquisition by forensic examiners. In this paper, we develop a TrustZone-based memory acquisition mechanism called TrustDump that is capable of reliably and securely obtaining the RAM memory and CPU registers of the mobile OS even if the OS has crashed or been compromised. TrustDump is isolated from the mobile OS by TrustZone. Instead of using a hypervisor to ensure the isolation between the OS and the memory acquisition tool, we rely on ARM TrustZone to achieve a hardware-assisted isolation with a small trusted computing base. TrustDump can include basic online analysis modules to catch malware in an early stage. Moreover, the acquired memory and register data can be sent to a remote server through a fast Micro-USB port for real-time forensics analysis when the OS runs or a slow serial port for further forensic analysis when the OS has crashed. A trusted graphical user interface is integrated in the TrustZone to authenticate the user and prevent the misuse of our memory acquisition tool. We build a TrustDump prototype on Freescale i.MX53 QSB.

Moving Target Defense (MTD) is emerging as a game changing approach consisting in a number of mechanisms that automatically change one or more system attributes in order to make a system's attack surface unpredictable to adversaries. The main objective of the First ACM Workshop on Moving Target Defense (MTD 2014) is to address the challenges of developing new MTD techniques and evaluating the effectiveness of MTD techniques with theoretical analysis and experimental results. This workshop aims to bring together researchers from academia, government, and industry to report on the latest research efforts on moving target defense,and to have productive discussion and constructive debate on this topic.

Smartphones are playing an increasingly important role in personal life and carrying massive private data. Unfortunately, once the smartphones are stolen, all the sensitive information, such as contacts, messages, photos, credit card information and passwords, may fall into the hands of malicious people. In order to protect the private data, remote deletion mechanism is required to allow owners to wipe the sensitive data on the stolen phone remotely. Existing remote deletion techniques rely on the availability of either WiFi for Internet connection or SIM card for cellular network connection; however, these requirements may not be satisfied when the phones are stolen by some sophisticated adversaries. In this paper, we propose a new remote deletion mechanism that allows the phone owner to delete the private data remotely even if the WiFi is disabled and the SIM card is unplugged. The basic idea is to use emergency call mechanisms to establish a communication connection with a service provider to verify the state of the phone and perform remote deletion. We present a case study of our mechanism with the Universal Mobile Telecommunications System (UMTS) network.

The advent of cloud computing and inexpensive multi-core desktop architectures has led to the widespread adoption of virtualization technologies. Furthermore, security researchers embraced virtual machine monitors (VMMs) as a new mechanism to guarantee deep isolation of untrusted software components, which, coupled with their popularity, promoted VMMs as a prime target for exploitation. In this paper, we present HyperCheck, a hardware-assisted tampering detection framework designed to protect the integrity of hypervisors and operating systems. Our approach leverages System Management Mode (SMM), a CPU mode in ×86 architecture, to transparently and securely acquire and transmit the full state of a protected machine to a remote server. We have implement two prototypes based on our framework design: HyperCheck-I and HyperCheck-II, that vary in their security assumptions and OS code dependence. In our experiments, we are able to identify rootkits that target the integrity of both hypervisors and operating systems. We show that HyperCheck can defend against attacks that attempt to evade our system. In terms of performance, we measured that HyperCheck can communicate the entire static code of Xen hypervisor and CPU register states in less than 90 million CPU cycles, or 90 ms on a 1 GHz CPU.

Virtual Machine Introspection (VMI) systems have been widely adopted for malware detection and analysis. VMI systems use hypervisor technology for system introspection and to expose malicious activity. However, recent malware can detect the presence of virtualization or corrupt the hypervisor state thus avoiding detection. We introduce SPECTRE, a hardware-assisted dependability framework that leverages System Management Mode (SMM) to inspect the state of a system. Contrary to VMI, our trusted code base is limited to BIOS and the SMM implementations. SPECTRE is capable of transparently and quickly examining all layers of running system code including a hypervisor, the OS, and user level applications. We demonstrate several use cases of SPECTRE including heap spray, heap overflow, and rootkit detection using real-world attacks on Windows and Linux platforms. In our experiments, full inspection with SPECTRE is 100 times faster than similar VMI systems because there is no performance overhead due to virtualization.

Distributed Denial of Service (DDoS) attacks still pose a significant threat to critical infrastructure and Internet services alike. In this paper, we propose MOTAG, a moving target defense mechanism that secures service access for authenticated clients against flooding DDoS attacks. MOTAG employs a group of dynamic packet indirection proxies to relay data traffic between legitimate clients and the protected servers. Our design can effectively inhibit external attackers' attempts to directly bombard the network infrastructure. As a result, attackers will have to collude with malicious insiders in locating secret proxies and then initiating attacks. However, MOTAG can isolate insider attacks from innocent clients by continuously "moving" secret proxies to new network locations while shuffling client-to-proxy assignments. We develop a greedy shuffling algorithm to minimize the number of proxy re- allocations (shuffles) while maximizing attack isolation. Simulations are used to investigate MOTAG's effectiveness on protecting services of different scales against intensified DDoS attacks.

Fingerprinting wireless devices using physical properties has been recently suggested as an alternative for device identification and authentication. It has been found that the clock skew caused by the frequency discrepancy of the quartz crystals in different devices can be used as a reliable source for fingerprinting. Researchers have studied the application of the clock skew-based fingerprinting in sensor networks and claimed that it can detect fake identities, wormholes, and node replicas. However, the study in this paper draws a completely opposite conclusion, i.e., the clock skew of sensor nodes can be easily forged by adversaries to evade the detection. This paper then studies the feasibility of using the distribution of signal power in space to fingerprint sensor nodes. The result shows that a sensor node's signal power distribution in space is not only reliable for being used as a source for fingerprinting but also very hard to forge. Finally, the paper discusses the application of using signal power distribution for detecting various attacks as well as the limitations and open problems.

Due to performance constraints, host intrusion detection defenses depend on event and polling-based tamper-proof mechanisms to detect security breaches. These defenses monitor the state of critical software components in an attempt to discover any deviations from a pristine or expected state. The rate and type of checks depend can be both periodic and event-based, for instance triggered by hardware events. In this paper, we demonstrate that all software and hardware-assisted defenses that analyze non-contiguous state to infer intrusions are fundamentally vulnerable to a new class of attacks, we call “evasion attacks”. We detail two categories of evasion attacks: directly-intercepting the defense triggering mechanism and indirectly inferring its periodicity. We show that evasion attacks are applicable to a wide-range of protection mechanisms and we analyze their applicability in recent state-of-the-art hardware-assisted protection mechanisms. Finally, we quantify the performance of implemented proof-of-concept prototypes for all of the attacks and suggest potential countermeasures.

Multi-tenancy of the cloud maximizes the utility of computation and storage resources by multiplexing the underlying hardware infrastructure amongst cloud customers; however, it also introduces significant security issues such as information leakage between two virtual machines (VMs) even if certain access control policy (e.g., Chinese Wall security policy) has been deployed in the cloud. Physical resource isolation between VMs is an effective mechanism to remove the covert channels in the cloud and prevent information leakage; however, due to economic concerns or negligence, some cheap-and-lazy cloud providers are not motivated to enforce the physical resource isolation as they promised. In this paper, we first develop a mechanism to check the co-residency of two files on local hard disk(s) by measuring the file access time, and then extend our mechanism to check data storage co-residency on Amazon S3 cloud storage.

This paper presents a capability-based security mechanism called CapMan. Our approach is designed to prevent Denial-of-Service (DoS) attacks on wireless communications, particularly against multi-path communication in Mobile Ad-hoc Networks (MANETs). CapMan offers a mechanism for a per flow, distributed bandwidth control by all the participating nodes along multiple communication paths. By exchanging summary capability messages, each node can maintain a global view of the overall throughput of flows in the network, and then dynamically adjust local constraints to prevent potential DoS attacks against a specific node or the network. Our approach is capable of scalably curtailing sophisticated DoS attacks that target multi-path routing protocols, even in the case that both the initiator and the responder of a network flow are malicious insiders and collude to deprive the network of valuable resources. We provide a theoretical analysis of our algorithms and also evaluate the protection and overhead of our prototype using AOMDV for routing.

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.

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.

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.

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.

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.