Malware attack has been a serious threat to the security and privacy of both individual and corporation users of the Android platform. Business entities seek to protect themselves by means of monitoring privacy-related sensitive behaviors conducted on company-issued Android devices. However, due to Android’s own access control and privacy protection policies, this is difficult to be done with third-party apps using only normal privileges. Existing works proposed using side-channel readings from leaky APIs and system virtual files to speculate runtime app behaviors, which could be unreliable due to future system updates (that ban exploited resources), hardware jittering, etc. In this paper, we argue that a more traditional side-channel attack strategy, namely the CPU-cache-based side channel, could be exploited in the benign scenario of app behavior surveillance. Specifically, we propose CacheAlarm, a sensitive app behavior monitor and foreground app identification system, which works by measuring cache side-channel readings of selected methods within the Android framework, and conducted in-lab and in-the-wild user studies to compare the effectiveness of our scheme against SideNet, a previous Android app behavior surveillance scheme using API-based side channels. Results of the studies suggested that CacheAlarm outperforms SideNet on the accuracy of detecting sensitive behaviors in addition to gaining the capability of detecting apps running at foreground of the user device.

The development of deep learning-based au-tonomous driving systems is becoming increasingly prevalent in recent times, however, several safety concerns have emerged. In certain uncommon situations, the generalization and robustness of deep learning have resulted in safety crises and accidents. To address this, it is crucial to comprehensively cover a range of possible conditions in simulators and identify risk scenarios within the system, which poses a significant high-dimensional search problem. To efficiently, accurately, and comprehensively generate diverse risk scenarios, we propose a scenario evaluation model. This model can learn the distribution of risk factors from a limited number of scenario samples and provide pre-evaluation to guide the generation process. The experimental results show that that our method can generate an average of 40.6% more risk scenarios compared to other generation methods, while also requiring 63.6% fewer simulations. The method based on the scenario evaluation model can effectively improve efficiency, accuracy, and the identification of more risk scenarios, thus providing a more thorough evaluation of the safety performance of autonomous driving systems.

Parallel fuzzing relies on hardware resources to guarantee test throughput and efficiency. In industrial practice, it is well known that parallel fuzzing faces the challenge of task division, but most works neglect the important process of corpus allocation. In this paper, we proposed a group-based corpus scheduling strategy to address these two issues, which has been accepted by the LLVM community. And we implement a parallel fuzzer based on this strategy called glibFuzzer. glibFuzzer first groups the global corpus into different subsets and then assigns different energy scores and different scores to them. The energy scores were mainly determined by the seed size and the length of coverage information, and the difference score can describe the degree of difference in the code covered by different subsets of seeds. In each round of key local corpus construction, the master node selects high-quality seeds by combining the two scores to improve test efficiency and avoid task conflict. To prove the effectiveness of the strategy, we conducted an extensive evaluation on the real-world programs and FuzzBench. After 4×24 CPU-hours, glibFuzzer covered 22.02% more branches and executed 19.42 times more test cases than libFuzzer in 18 real-world programs. glibFuzzer showed an average branch coverage increase of 73.02%, 55.02%, 55.86% over AFL, PAFL, UniFuzz, respectively. More importantly, glibFuzzer found over 100 unique vulnerabilities.

Graphics Processing Units (GPUs) are now a key component of many devices and systems, including those in the cloud and data centers, thus are also subject to side-channel attacks. Existing side-channel attacks on GPUs typically leak information from graphics libraries like OpenGL and CUDA, which require creating contentions within the GPU resource space and are being mitigated with software patches. This article evaluates potential side channels exposed at a lower-level interface between GPUs and CPUs, namely the graphics interrupts. These signals could indicate unique signatures of GPU workload, allowing a spy process to infer the behavior of other processes. We demonstrate the practicality and generality of such side-channel exploitation with a variety of assumed attack scenarios. Simulations on both Nvidia and Intel graphics adapters showed that our attack could achieve high accuracy, while in-depth studies were also presented to explore the low-level rationale behind such effectiveness. On top of that, we further propose a practical mitigation scheme which protects GPU workloads against the graphics-interrupt-based side-channel attack by piggybacking mask payloads on them to generate interfering graphics interrupt “noises”. Experiments show that our mitigation technique effectively prohibited spy processes from inferring user behaviors via analyzing runtime patterns of graphics interrupt with only trivial overhead.

Android as an operating system is now increasingly being adopted in industrial information systems, especially with cyber-physical systems (CPS). This also puts Android devices onto the front line of handling security-related data and conducting sensitive behaviors, which could be misused by the increasing number of polymorphic and metamorphic malicious applications targeting the platform. The existence of such malware threats, therefore, call for more accurate identification and surveillance of sensitive Android app behaviors, which is essential to the security of CPS and Internet of Things (IoT) devices powered by Android. Nevertheless, achieving dynamic app behavior monitoring and identification on real CPS powered by Android is challenging because of restrictions from the security and privacy model of the platform. In this article, the authors investigate how the latest advances in deep learning could address this security problem with better accuracy. Specifically, a deep learning engine is proposed that detects sensitive app behaviors by classifying patterns of system-wide statistics, such as available storage space and transmitted packet volume, using a customized deep neural network based on existing models called Encoder and ResNet. Meanwhile, to handle resource limitations on typical CPS and IoT devices, sparse learning is adopted to reduce the amount of valid parameters in the trained neural network. Evaluations show that the proposed model outperforms a well-established group of baselines on time series classification in identifying sensitive app behaviors with background noise and the targeted behaviors potentially overlapping.

Human activity recognition based on wireless signals emerges as a research hotspot recently. Though tremendous efforts have been devoted and significant progresses have been achieved, one fundamental issue still remains open, i.e., theoretical modeling between signal dynamics and human activities. This paper fills in the blank by addressing several theoretical issues and providing insightful mathematical analysis including a signal-activity model. To validate such analysis, a prototype system has been built, where a series of real-world experiments has been conducted. Empirical results have justified our theoretical findings. Moreover, important hands-on experiences on the system implementation and parameter settings have been offered.