Biblio

The evolution of smart automobiles and vehicles within the Internet of Things (IoT) - particularly as that evolution leads toward a proliferation of completely autonomous vehicles - has sparked considerable interest in the subject of vehicle/automotive security. While the attack surface is wide, there are patterns of exploitable vulnerabilities. In this study we reviewed, classified according to their attack surface and evaluated some of the common vehicle and infrastructure attack vectors identified in the literature. To remediate these attack vectors, specific technical recommendations have been provided as a way towards secure deployments of smart automobiles and transportation infrastructures.

The domain of securing in-vehicle networks has attracted both academic and industrial researchers due to high danger of attacks on drivers and passengers. While securing wired and wireless interfaces is important to defend against these threats, detecting attacks is still the critical phase to construct a robust secure system. There are only a few results on securing communication inside vehicles using anomaly-detection techniques despite their efficiencies in systems that need real-time detection. Therefore, we propose an intrusion detection system (IDS) based on Multi-Layer Perceptron (MLP) neural network for Controller Area Networks (CAN) bus. This IDS divides data according to the ID field of CAN packets using K-means clustering algorithm, then it extracts suitable features and uses them to train and construct the neural network. The proposed IDS works for each ID separately and finally it combines their individual decisions to construct the final score and generates alert in the presence of attack. The strength of our intrusion detection method is that it works simultaneously for two types of attacks which will eliminate the use of several separate IDS and thus reduce the complexity and cost of implementation.

Wireless Sensor Network is the combination of small devices called sensor nodes, gateways and software. These nodes use wireless medium for transmission and are capable to sense and transmit the data to other nodes. Generally, WSN composed of two types of nodes i.e. generic nodes and gateway nodes. Generic nodes having the ability to sense while gateway nodes are used to route that information. IoT now extended to IoET (internet of Everything) to cover all electronics exist around, like a body sensor networks, VANET's, smart grid stations, smartphone, PDA's, autonomous cars, refrigerators and smart toasters that can communicate and share information using existing network technologies. The sensor nodes in WSN have very limited transmission range as well as limited processing speed, storage capacities and low battery power. Despite a wide range of applications using WSN, its resource constrained nature given birth to a number severe security attacks e.g. Selective Forwarding attack, Jamming-attack, Sinkhole attack, Wormhole attack, Sybil attack, hello Flood attacks, Grey Hole, and the most dangerous BlackHole Attacks. Attackers can easily exploit these vulnerabilities to compromise the WSN network.

Safety and non-safety applications in the external communication systems of self-driving vehicles require authentication of control data, cooperative awareness messages and notification messages. Traditional security systems can prevent attackers from hacking or breaking important system functionality in autonomous vehicles. This paper presents a novel security system designed to protect vehicular ad hoc networks in self-driving and semi-autonomous vehicles that is based on Integrated Circuit Metric technology (ICMetrics). ICMetrics has the ability to secure communication systems in autonomous vehicles using features of the autonomous vehicle system itself. This security system is based on unique extracted features from vehicles behaviour and its sensors. Specifically, features have been extracted from bias values of infrared sensors which are used alongside semantically extracted information from a trace file of a simulated vehicular ad hoc network. The practical experimental implementation and evaluation of this system demonstrates the efficiency in identifying of abnormal/malicious behaviour typical for an attack.