In-Vehicle Data Communication with CAN &Security Monitoring: A Review

Automobiles are now being created with more electronic components for efficient functioning such as Anti Lock Braking system, Adaptive Cruise Control, Traction control system, Airbag, Power Steering etc. managed by networked controllers that include hundreds of ECUs (electronic control units) that can coordinate, control, and monitor loads of internal vehicle components. Each component, such as ABS, TCS (Traction control system), tire pressure monitoring system and telematics system, may communicate with nearby components over the CAN (Controller Area Network) bus, establishing an in-vehicle communication network. These modern automobile system networks intended for safety with minimal consideration for security have drawn the attention of researchers for providing security in CAN. The Paper reviews the behavior and vulnerabilities of CAN within an in-vehicle network including various attacks possible in CAN along with the proposed solutions in the literature with extensive survey on a security promising approach named as IDS (Intrusion detection system)

A taxonomy and survey of cyber-physical intrusion detection approaches for vehicles

With the growing threat of cyber and cyber-physical attacks against automobiles, drones, ships, driverless pods and other vehicles, there is also a growing need for intrusion detection approaches that can facilitate defence against such threats. Vehicles tend to have limited processing resources and are energy-constrained. So, any security provision needs to abide by these limitations. At the same time, attacks against vehicles are very rare, often making knowledge-based intrusion detection systems less practical than behaviour-based ones, which is the reverse of what is seen in conventional computing systems. Furthermore, vehicle design and implementation can differ wildly between different types or different manufacturers, which can lead to intrusion detection designs that are vehicle-specific. Equally importantly, vehicles are practically defined by their ability to move, autonomously or not. Movement, as well as other physical manifestations of their operation may allow cyber security breaches to lead to physical damage, but can also be an opportunity for detection. For example, physical sensing can contribute to more accurate or more rapid intrusion detection through observation and analysis of physical manifestations of a security breach. This paper presents a classification and survey of intrusion detection systems designed and evaluated specifically on vehicles and networks of vehicles. Its aim is to help identify existing techniques that can be adopted in the industry, along with their advantages and disadvantages, as well as to identify gaps in the literature, which are attractive and highly meaningful areas of future research

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