A Study of Automotive Security : CAN Bus Intrusion detection Systems, Attack Surface, and Regulations

The innovation in the automotive sector enhanced the technology implemented in vehicles by the manufacturers. Consequently, the overall driving experience improved, thanks to the introduction of better safety, utility, and entertainment systems. Moreover, automobiles began collecting and exchanging data with the external world through different communication protocols. However, these additions have started to attract attention from security experts. More importantly, malevolent attackers have exploited the technologies and their related attack points to carry out malicious activities to cause data security and safety issues. These issues have led to establishing standards and regulations (ISO 21434, UNECE 155, etc.) that redefine vehicle design and development by incorporating security protocols and requirements necessary to create secure automobiles. However, these documents analyze the problem at a high level and do not dwell on practical solutions implementation analysis. This work presents an in-depth study of in-vehicle communication concerns via Controller Area Network (CAN) bus safety problems analysis with different proposed solutions. Specifically, a survey of Intrusion Detection Systems developed in the literature is brought up: simulation of three CAN bus intrusion detection systems against various attacks. The results show effectiveness against disruptive attacks, i.e., with numerous messages sent in a short period of time, but conversely have difficulty detecting more targeted attacks with few transmitted packets. The solutions analysis is an excellent starting point for security engineers to be able to develop Intrusion Detection Systems for the CAN bus capable of detecting attacks that will become increasingly complex and difficult to counter over time

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Tree-based Intelligent Intrusion Detection System in Internet of Vehicles

The use of autonomous vehicles (AVs) is a promising technology in Intelligent Transportation Systems (ITSs) to improve safety and driving efficiency. Vehicle-to-everything (V2X) technology enables communication among vehicles and other infrastructures. However, AVs and Internet of Vehicles (IoV) are vulnerable to different types of cyber-attacks such as denial of service, spoofing, and sniffing attacks. In this paper, an intelligent intrusion detection system (IDS) is proposed based on tree-structure machine learning models. The results from the implementation of the proposed intrusion detection system on standard data sets indicate that the system has the ability to identify various cyber-attacks in the AV networks. Furthermore, the proposed ensemble learning and feature selection approaches enable the proposed system to achieve high detection rate and low computational cost simultaneously.Comment: Accepted in IEEE Global Communications Conference (GLOBECOM) 201