Biblio

The Controller Area Network (CAN) bus is the de-facto standard for connecting the Electronic Control Units (ECUs) in automobiles. However, there are serious cyber-security risks due to the lack of security mechanisms. In order to mine the vulnerabilities in CAN bus, this paper proposes CAN-FT, a fuzz testing method for automotive CAN bus, which uses a Generative Adversarial Network (GAN) based fuzzy message generation algorithm and the Adaptive Boosting (AdaBoost) based anomaly detection mechanism to capture the abnormal states of CAN bus. Experimental results on a real-world vehicle show that CAN-FT can find vulnerabilities more efficiently and comprehensively.

The public key infrastructure (PKI) based authentication protocol provides the basic security services for vehicular ad-hoc networks (VANETs). However, trust and privacy are still open issues due to the unique characteristics of vehicles. It is crucial for VANETs to prevent internal vehicles from broadcasting forged messages while simultaneously protecting the privacy of each vehicle against tracking attacks. In this paper, we propose a blockchain-based anonymous reputation system (BARS) to break the linkability between real identities and public keys to preserve privacy. The certificate and revocation transparency is implemented efficiently using two blockchains. We design a trust model to improve the trustworthiness of messages relying on the reputation of the sender based on both direct historical interactions and indirect opinions about the sender. Experiments are conducted to evaluate BARS in terms of security and performance and the results show that BARS is able to establish distributed trust management, while protecting the privacy of vehicles.