Unmanned Aerial Vehicles (UAVs) have a wide range of capacities, including surveillance, rescue missions, geographi-cal mapping, and military uses. This work focuses on combining computer vision with route planning methods, namely DragFly, to ensure the safety of a UAV throughout its whole flying mission to a designated target. We use an advanced object detection algorithm to precisely identify the locations of obstacles and create an accurate geographical map. Two path-planning algorithms are introduced to improve flying safety. We enhance the A* algorithm to include searching in hazardous areas, allowing the UAV to stay a safe distance away from dangers such as anti-drones and preserve communication. Furthermore, our tangent point approach may provide extremely fast searching performance in small to medium map sizes. The evaluation results demonstrate that our approach can locate the shortest path in just 31.66 milliseconds without compromising safety.

The Denial-of-Service (DoS) attacks have rapidly increased over the years, particularly from the Internet of Things (IoT) devices such as connected IP cameras/vehicles and IP door entries. As a result of a lack of strong security implementation, these low-cost IoT devices can become zombie bots by brute force (using a password dictionary) and malware injection. Thousands of such zombies have been a powerful tool to initiate DoS attacks that can consume any target network's bandwidth. Recently, Deep Learning (DL) based methods have achieved admirable performance to mitigate DoS attacks significantly. However, besides the high latency of processing huge volumes of incoming traffic, current D L-based methods are often designed based on crafting a model carefully, which costs the developers significant time and effort. This paper introduces a novel automated deep-learning (autoDL) scheme to automatically generate an efficient DoS defense model. The system can find the best suitable detection model and a detailed configuration that is lightweight enough to deploy at IoT gateways/wireless routers/programmable switches/edge servers near the attack sources. To our knowledge, this is the first attempt to develop such an autoDL platform for DoS filter generation. The evaluation results show that the defense system generated by autoDL can ease 99.7% of malicious traffic before they go out to the Internet with only 0.593 ms for request reaction, a promising performance compared to the literature.

Non-termination is the root cause of a variety of program bugs, such as hanging programs and denial-of-service vulnerabilities. This makes an automated analysis that can prove the absence of such bugs highly desirable. To scale termination checks to large systems, an interprocedural termination analysis seems essential. This is a largely unexplored area of research in termination analysis, where most effort has focussed on small but difficult single-procedure problems. We present a modular termination analysis for C programs using template-based interprocedural summarisation. Our analysis combines a context-sensitive, over-approximating forward analysis with the inference of under-approximating preconditions for termination. Bit-precise termination arguments are synthesised over lexicographic linear ranking function templates. Our experimental results show the advantage of interprocedural reasoning over monolithic analysis in terms of efficiency, while retaining comparable precision.

Proving program termination is key to guaranteeing absence of undesirable behaviour, such as hanging programs and even security vulnerabilities such as denial-of-service attacks. To make termination checks scale to large systems, interprocedural termination analysis seems essential, which is a largely unexplored area of research in termination analysis, where most effort has focussed on difficult single-procedure problems. We present a modular termination analysis for C programs using template-based interprocedural summarisation. Our analysis combines a context-sensitive, over-approximating forward analysis with the inference of under-approximating preconditions for termination. Bit-precise termination arguments are synthesised over lexicographic linear ranking function templates. Our experimental results show that our tool 2LS outperforms state-of-the-art alternatives, and demonstrate the clear advantage of interprocedural reasoning over monolithic analysis in terms of efficiency, while retaining comparable precision.