Biblio

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.

Automotive systems are currently undergoing a rapid evolution through the integration of the Internet of Things (IoT) and Software Defined Networking (SDN) technologies. The main focus of this evolution is to improve the driving experience, including automated controls, intelligent navigation and safety systems. Moreover, the extremely rapid pace that such technologies are brought into the vehicles, necessitates the presence of adequate testing of new features to avoid operational errors. Apart from testing though, IoT and SDN technologies also widen the threat landscape of cyber-security risks due to the amount of connectivity interfaces that are nowadays exposed in vehicles. In this paper we present a new method, based on OMNET++, for testing new in-vehicle features and assessing security risks through network simulation. The method is demonstrated through a case-study on a Toyota Prius, whose network data are analyzed for the detection of anomalies caused from security threats or operational errors.

In-vehicle networks like Controller Area Network, FlexRay, Ethernet are now subjected to huge security threats where unauthorized entities can take control of the whole vehicle. This can pose very serious threats including accidents. Security features like encryption, message authentication are getting implemented in vehicle networks to counteract these issues. This paper is proposing a set of novel validation techniques to ensure that vehicle network security is fool proof. Security validation against requirements, security validation using white box approach, black box approach and grey box approaches are put forward. Test system architecture, validation of message authentication, decoding the patterns from vehicle network data, using diagnostics as a security loophole, V2V V2X loopholes, gateway module security testing are considered in detail. Aim of this research paper is to put forward a set of tools and methods for finding and reporting any security loopholes in the in-vehicle network security implementation.