Jiyang Chen

Jiyang Chen, Zhiwei Feng, Jen-Yang Wen et al.

The Unmanned aerial vehicles (UAVs) sector is fast-expanding. Protection of real-time UAV applications against malicious attacks has become an urgent problem that needs to be solved. Denial-of-service (DoS) attack aims to exhaust system resources and cause important tasks to miss deadlines. DoS attack may be one of the common problems of UAV systems, due to its simple implementation. In this paper, we present a software framework that offers DoS attack-resilient control for real-time UAV systems using containers: ContainerDrone. The framework provides defense mechanisms for three critical system resources: CPU, memory, and communication channel. We restrict attacker's access to CPU core set and utilization. Memory bandwidth throttling limits attacker's memory usage. By simulating sensors and drivers in the container, a security monitor constantly checks DoS attacks over communication channels. Upon the detection of a security rule violation, the framework switches to the safety controller to mitigate the attack. We implemented a prototype quadcopter with commercially off-the-shelf (COTS) hardware and open-source software. Our experimental results demonstrated the effectiveness of the proposed framework defending against various DoS attacks.

Jiyang Chen, Simon Yu, Rohan Tabish et al.

Object detection in state-of-the-art Autonomous Vehicles (AV) framework relies heavily on deep neural networks. Typically, these networks perform object detection uniformly on the entire camera LiDAR frames. However, this uniformity jeopardizes the safety of the AV by giving the same priority to all objects in the scenes regardless of their risk of collision to the AV. In this paper, we present a new end-to-end pipeline for AV that introduces the concept of LiDAR cluster first and camera inference later to detect and classify objects. The benefits of our proposed framework are twofold. First, our pipeline prioritizes detecting objects that pose a higher risk of collision to the AV, giving more time for the AV to react to unsafe conditions. Second, it also provides, on average, faster inference speeds compared to popular deep neural network pipelines. We design our framework using the real-world datasets, the Waymo Open Dataset, solving challenges arising from the limitations of LiDAR sensors and object detection algorithms. We show that our novel object detection pipeline prioritizes the detection of higher risk objects while simultaneously achieving comparable accuracy and a 25% higher average speed compared to camera inference only.

Jiyang Chen, Tomasz Kloda, Ayoosh Bansal et al.

Real-time systems have recently been shown to be vulnerable to timing inference attacks, mainly due to their predictable behavioral patterns. Existing solutions such as schedule randomization lack the ability to protect against such attacks, often limited by the system's real-time nature. This paper presents SchedGuard: a temporal protection framework for Linux-based hard real-time systems that protects against posterior scheduler side-channel attacks by preventing untrusted tasks from executing during specific time segments. SchedGuard is integrated into the Linux kernel using cgroups, making it amenable to use with container frameworks. We demonstrate the effectiveness of our system using a realistic radio-controlled rover platform and synthetically generated workloads. Not only is SchedGuard able to protect against the attacks mentioned above, but it also ensures that the real-time tasks/containers meet their temporal requirements.