No abstract

No abstract

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.

Wireless sensor networks (WSNs) are gaining more and more interest in the research community due to their unique characteristics. Besides energy consumption considerations, security has emerged as an equally important aspect in their network design. This is because WSNs are vulnerable to various types of attacks and to node compromises, and as such, they require security mechanisms to defend against them. An intrusion detection system (IDS) is one such solution to the problem. While several signature-based and anomaly-based detection algorithms have been proposed to date for WSNs, none of them is specifically designed for the ultra-wideband (UWB) radio technology. UWB is a key solution for wireless connectivity among inexpensive devices characterized by ultra-low power consumption and high precision ranging. Based on these principles, in this paper, we propose a novel anomaly-based detection and location-attribution algorithm for cluster-based UWB WSNs. The proposed algorithm, abbreviated as ADLU, has dedicated procedures for secure cluster formation, periodic re-clustering, and efficient cluster member monitoring. The performance of ADLU in identifying and localizing intrusions using a rule-based anomaly detection scheme is studied via simulations.

No abstract