Intelligent Reflecting Surface (IRS) enabled Unmanned Aerial Vehicle (UAV) edge computing, a new communication technology, can provide sufficient capacity for edge computing system. However, due to the Line-of-Sight (LoS) or the Non Line of Sight (NLoS) of communicating environments will impact transmitting rate or delay, the Intelligent Reflective Surface (IRS) can be utilized to compensate the channel fading in the IRS-enabled UAV edge computing. In this paper, the joint problem of IRS phase shift, UAV trajectory and power allocation in the system is investigated, aiming to maximize the energy efficient. The corresponding optimization problem, which consists of mixed integer nonlinear programming problem, is formulated. To solve the problem, the original problem is decomposed into two subproblems, and an iterative method framework based on ConVex optimization and Deep Reinforcement Learning (CV-DRL) is proposed. Given the UAV trajectory and IRS phase shift, the Convex optimization algorithm is used to solve the power allocation schemes. Then, given the power allocation schemes, the Double Deep Q Network (Double DQN) and Deep Deterministic Policy Gradient (DDPG) algorithms are utilized to solve the problem of optimal UAV trajectory and IRS phase shift. The simulation results demonstrate that our proposed method outperforms other schemes in terms of energy efficiency, providing significant enhancements

Automated vulnerability assessment and exploit generation for computing systems have been explored for decades. However, these approaches are incomplete in assessing industrial control systems, where networks of computing devices and physical processes interact for safety-critical missions. We present an attack synthesis algorithm against such cyber-physical electricity grids. The algorithm explores both discrete network configurations and continuous dynamics of the plant's embedded control system to search for attack strategies that evade detection with conventional monitors. The algorithm enabling this exploration is rooted in recent developments in the hybrid system verification research: it effectively approximates the behavior of the system for a set of possible attacks by computing sensitivity of the system's response to variations in the attack parameters. For parts of the attack space, the proposed algorithm can infer whether or not there exists a feasible attack that avoids triggering protection measures such as relays and steady-state monitors. The algorithm can take into account constraints on the attack space such as the power system topology and the set of controllers across the plant that can be compromised without detection. With a proof-of-concept prototype, we demonstrate the synthesis of transient attacks in several typical electricity grids and analyze the robustness of the synthesized attacks to perturbations in the network parameters.

Cyber-physical systems (CPSs) may interact and manipulate objects in the physical world, and therefore formal guarantees about their behavior are strongly desired. Static-time proofs of safety invariants, however, may be intractable for systems with distributed physical-world interactions. This is further complicated when realistic communication models are considered, for which there may not be bounds on message delays, or even when considering that messages will eventually reach their destination. In this work, we address the challenge of proving safety and progress in distributed CPSs communicating over an unreliable communication layer. We show that for this type of communication model, system safety is closely related to the results of a hybrid system’s reachability computation, which can be computed at runtime. However, since computing reachability at runtime may be computationally intensive, we provide an approach that moves significant parts of the computation to design time. This approach is demonstrated with a case study of a simulation of multiple vehicles moving within a shared environment.

We present a modular technique for simulation-based bounded verification for nonlinear dynamical systems. We introduce the notion of input-to-state discrepancy of each subsystem Ai in a larger nonlinear dynamical system A which bounds the distance between two (possibly diverging) trajectories of Ai in terms of their initial states and inputs. Using the IS discrepancy functions, we construct a low dimensional deterministic dynamical system M(δ). For any two trajectories of A starting δ distance apart, we show that one of them bloated by a factor determined by the trajectory of M contains the other. Further, by choosing appropriately small δ's the overapproximations computed by the above method can be made arbitrarily precise. Using the above results we develop a sound and relatively complete algorithm for bounded safety verification of nonlinear ODEs. Our preliminary experiments with a prototype implementation of the algorithm show that the approach can be effective for verification of nonlinear models.

Cyber-physical systems (CPS) may interact and manipulate objects in the physical world, and therefore ideally would have formal guarantees about their behavior. Performing static-time proofs of safety invariants, however, may be intractable for systems with distributed physical-world interactions. This is further complicated when realistic communication models are considered, for which there may not be bounds on message delays, or even that messages will eventually reach their destination. In this work, we address the challenge of proving safety and progress in distributed CPS communicating over an unreliable communication layer. This is done in two parts. First, we show that system safety can be verified by partially relying upon run-time checks, and that dropping messages if the run-time checks fail will maintain safety. Second, we use a notion of compatible action chains to guarantee system progress, despite unbounded message delays. We demonstrate the effectiveness of our approach on a multi-agent vehicle flocking system, and show that the overhead of the proposed run-time checks is not overbearing.

This paper presents an algorithm which uses simulation traces and formal models for computing overapproximations of reach sets of deterministic hybrid systems. The implementation of the algorithm in a tool, Hybrid Trace Verifier (HTV), uses Mathwork's Simulink/Stateflow (SLSF) environment for generating simulation traces and for obtaining formal models. Computation of the overapproximation relies on computing error bounds in the dynamics obtained from the formal model. Verification results from three case studies, namely, a version of the navigation benchmark, an engine control system, and a satellite system suggest that this combined formal analysis and simulation based approach may scale to larger problems.