Biblio

Cyber-Physical Systems(CPS) have been experiencing a fast-growing process in recent decades, and related security issues also have become more important than ever before. To design an efficient defensive policy for operators and controllers is the utmost task to be considered. In this paper, a stochastic game-theoretic model is developed to study a CPS security problem by considering the interdependence between cyber and physical spaces of a CPS. The game model is solved with Minimax Q-learning for finding the mixed strategies equilibria. The numerical simulation revealed that the defensive factors and attack cost can affect the policies adopted by the system. From the perspective of the operator of a CPS, increasing successful defense probability in the phrase of disruption will help to improve the probability of defense strategy when there is a correlation between the cyber layer and the physical layer in a CPS. On the contrary side, the system defense probability will decrease as the total cost of the physical layer increases.

Attack detection and recovery are fundamental elements for the operation of safe and resilient cyber-physical systems. Most of the literature focuses on attack-detection, while leaving attack-recovery as an open problem. In this paper, we propose novel attack-recovery control for securing cyber-physical systems. Our recovery control consists of new concepts required for a safe response to attacks, which includes the removal of poisoned data, the estimation of the current state, a prediction of the reachable states, and the online design of a new controller to recover the system. The synthesis of such recovery controllers for cyber-physical systems has barely investigated so far. To fill this void, we present a formal method-based approach to online compute a recovery control sequence that steers a system under an ongoing sensor attack from the current state to a target state such that no unsafe state is reachable on the way. The method solves a reach-avoid problem on a Linear Time-Invariant (LTI) model with the consideration of an error bound $ε$ $\geq$ 0. The obtained recovery control is guaranteed to work on the original system if the behavioral difference between the LTI model and the system's plant dynamics is not larger than $ε$. Since a recovery control should be obtained and applied at the runtime of the system, in order to keep its computational time cost as low as possible, our approach firstly builds a linear programming restriction with the accordingly constrained safety and target specifications for the given reach-avoid problem, and then uses a linear programming solver to find a solution. To demonstrate the effectiveness of our method, we provide (a) the comparison to the previous work over 5 system models under 3 sensor attack scenarios: modification, delay, and reply; (b) a scalability analysis based on a scalable model to evaluate the performance of our method on large-scale systems.

In view of the security threats caused by the code execution vulnerability of the industrial control system, design the trusted security architecture of the industrial control system based on the embedded system. From the trusted startup of industrial control equipment, the safety protection for industrial control system is completed. The scheme is based on TPM and Xilinx Zynq-7030 to build an industrial trusted computing environment and complete the trusted startup process. Experiment shows that this method can effectively prevent the destruction of malicious code during the startup process of embedded system and provide technical support for the construction of trusted computing environment of industrial control system.

Nowadays, the Network on Chip (NoC) is widely adopted by multi-core System on Chip (SoC) to meet its communication needs. With the gradual popularization of the Internet of Things (IoT), the application of NoC is increasing. Due to its distribution characteristics on the chip, NoC has gradually become the focus of potential security attacks. Denial of service (DoS) is a typical attack and it is caused by malicious intellectual property (IP) core with unnecessary data packets causing communication congestion and performance degradation. In this article, we propose a novel approach to detect DoS attacks on-line based on random forest algorithm, and detect the router where the attack enters the sensitive communication path. This method targets malicious third-party vendors to implant a DoS Hardware Trojan into the NoC. The data set is generated based on the behavior of multi-core routers triggered by normal and Hardware Trojans. The detection accuracy of the proposed scheme is in the range of 93% to 94%.

Android privacy control is an important but difficult problem to solve. Previously, there was much research effort either focusing on extending the Android permission model with better policies or modifying the Android framework for fine-grained access control. In this work, we take an integral approach by designing and implementing SweetDroid, a calling-context-sensitive privacy policy enforcement framework. SweetDroid combines automated policy generation with automated policy enforcement. The automatically generated policies in SweetDroid are based on the calling contexts of privacy sensitive APIs; hence, SweetDroid is able to tell whether a particular API (e.g., getLastKnownLocation) under a certain execution path is leaking private information. The policy enforcement in SweetDroid is also fine-grained - it is at the individual API level, not at the permission level. We implement and evaluate the system based on thousands of Android apps, including those from a third-party market and malicious apps from VirusTotal. Our experiment results show that SweetDroid can successfully distinguish and enforce different privacy policies based on calling contexts, and the current design is both developer hassle-free and user transparent. SweetDroid is also efficient because it only introduces small storage and computational overhead.