Communication in modern cars is managed by a controller area network (CAN) bus protocol and its extensions for electronic control units (ECUs). The CAN bus is a preferred method for reliable real-time broadcast communication. However, unprotected CAN communications make the vehicles vulnerable to a variety of practical malicious wired/wireless attacks. In this work, we analyze the existing frame-level authentication protocol and identify weaknesses and limitations. To address this, we provide a protocol suite for entity authentication, key management, a secure message flow for remote transmission request frames and session key update to be applied for vehicle connection with external devices. We prove the security of our protocol in the random oracle model and assess its resistance against known attacks. We formally verify the security of our protocol using the Tamarin tool. Our simulation results indicate that our protocol improves efficiency.

In recent decades, cyber security issues in IEC 61850-compliant substation automation systems (SASs) have become growing concerns. Many researchers have developed various strategies to detect malicious behaviours of SASs during the system operational stage, such as anomaly-based detection. However, most existing anomaly-based detection methods identify an abnormal behaviour by checking every single network packet without any association. These traditional methods cannot effectively detect “stealthy” attacks which modify legitimate messages slightly while imitating patterns of benign behaviours. In this paper, we present feature selection and extraction methods to generalise and summarise critical features when detecting insider attacks triggering from untrusted control devices within SASs. By applying a sliding window-based sequential classification mechanism, our detection method can detect anomalies across multiple devices without the need to learn datasets collected from all devices. Firstly, to generalise critical features and summarise systems’ behaviours so that it is unnecessary to collect all datasets, we selected and extracted six critical network features from generic object-oriented substation events (GOOSE) messages and seven summarised physical features based on the general architecture of the primary plant of distribution substations. After that, to improve detection accuracy and reduce computational costs, we applied sliding window algorithms to divide datasets into different overlapped window-based snippets. Then we applied a sequential classification model based on Bidirectional Long Short-Term Memory networks to train and test those datasets. As a result, our method can detect insider attacks across multiple devices accurately with a false-negative rate of less than 1%.

In intelligent vehicular networks, vehicles have enhanced sensing capabilities and carry computing and communication platforms to enable new versatile systems known as Vehicular Communication (VC) systems. Vehicles communicate with other vehicles and with nearby fixed equipment to support different applications, including those which increase driver awareness of the surroundings. This should result in improved safety and may optimize traffic. However, VC systems are vulnerable to cyber attacks involving message manipulation. Research aimed at tackling this problem has resulted in the proposal of multiple authentication protocols. Several existing survey papers have attempted to classify some of these protocols based on a limited set of characteristics. However, to date there is no generic framework to support the comparison of these protocols and provide guidance for design and evaluation. Most existing classifications either use computation complexity of cryptographic techniques as a criterion, or they fail to make connections between different important aspects of authentication. This paper provides such a framework, proposing a new taxonomy to enable a consistent means of classifying authentication schemes based upon seven main criteria. The main contribution of this study is a framework to enable protocol designers and investigators to adequately compare and select authentication schemes when deciding on particular protocols to implement in an application. Our framework can be applied in design, making choices appropriate for the intended context in both intra-vehicle and inter-vehicle communications. We demonstrate the application of our framework using two different types of case study: individual analysis and hypothetical design. Additionally, this work makes several related contributions. We present the network model, outline the applications, list the communication patterns and the underlying standards, and discuss the necessity of using cryptography and key management in VC systems. We also review the threats, authentication, and privacy requirements in vehicular networks.

Standards such as the American IEEE 1609, European ETSI ITS-G5, and Japanese ARIB STD-T109 aim to establish Cooperative Intelligent Transportation Systems (C-ITS) by enabling Vehicular Ad-Hoc Networks (VANETs). In VANETs, vehicles communicate with other vehicles and roadside infrastructure to support latency-critical applications which increase driver awareness of the surroundings. This should result in improved safety and possibly optimizing traffic. However, to secure VANET communications against message manipulation or replaying, security standards such as IEEE 1609.2 and ETSI TS 103 097 are proposed. In this work, we implement the cryptographic primitives recommended in the IEEE 1609.2 standard to authenticate low latency safety critical messages. We evaluate the effect of the implementation using metrics such as CPU clock cycles per operation, average computation time in milliseconds, and message size in bits. We perform a simulation presenting a high-density highway scenario for the above mentioned C-ITS standards. For each standard, we evaluate the number of safety messages that can be successfully received within 100 ms latency. We show how and to what extent the authentication overhead of latency-critical messages may impact on driver safety. Under an assumed traffic scenario, we show that a crash is possible, as a result of the evaluated authentication delay. We show that the recommended algorithms with specific parameters can be a potential solution for low latency safety-critical applications in a large scale scenario.