Intelligent intrusion detection in external communication systems for autonomous vehicles

Self-driving vehicles are known to be vulnerable to different types of attacks due to the type of communication systems which are utilized in these vehicles. These vehicles are becoming more reliant on external communication through vehicular ad hoc networks. However, these networks contribute new threats to self-driving vehicles which lead to potentially significant problems in autonomous systems. These communication systems potentially open self-driving vehicles to malicious attacks like the common Sybil attacks, black hole, Denial of Service, wormhole attacks and grey hole attacks. In this paper, an intelligent protection mechanism is proposed, which was created to secure external communications for self-driving and semi-autonomous cars. The protection mechanism is based on the Proportional Overlapping Scores method, which allows to decrease the number of features found in the Kyoto benchmark dataset. This hybrid detection system uses Back Propagation neural networks to detect Denial of Service (DoS), a common type of attack in vehicular ad hoc networks. The results from our experiment revealed that the proposed intrusion detection has the ability to identify malicious vehicles in self-driving and even in semi-autonomous vehicles

A hierarchical detection method in external communication for self-driving vehicles based on TDMA

Security is considered a major challenge for self-driving and semi self-driving vehicles. These vehicles depend heavily on communications to predict and sense their external environment used in their motion. They use a type of ad hoc network termed Vehicular ad hoc networks (VANETs). Unfortunately, VANETs are potentially exposed to many attacks on network and application level. This paper, proposes a new intrusion detection system to protect the communication system of self-driving cars; utilising a combination of hierarchical models based on clusters and log parameters. This security system is designed to detect Sybil and Wormhole attacks in highway usage scenarios. It is based on clusters, utilising Time Division Multiple Access (TDMA) to overcome some of the obstacles of VANETs such as high density, high mobility and bandwidth limitations in exchanging messages. This makes the security system more efficient, accurate and capable of real time detection and quick in identification of malicious behaviour in VANETs. In this scheme, each vehicle log calculates and stores different parameter values after receiving the cooperative awareness messages from nearby vehicles. The vehicles exchange their log data and determine the difference between the parameters, which is utilised to detect Sybil attacks and Wormhole attacks. In order to realize efficient and effective intrusion detection system, we use the well-known network simulator (ns-2) to verify the performance of the security system. Simulation results indicate that the security system can achieve high detection rates and effectively detect anomalies with low rate of false alarms