Connected autonomous vehicles (CAVs) will be deployed over the next decade with autonomous functionalities supported by new sensing and communication capabilities. Such functionality exposes CAVs to new attacks that current vehicles will not face. To ensure the safety and security of CAVs, it is important to be able to identify the ways in which the system could be attacked and to build defences against these attacks. One possible approach is to use reference architectures to perform an attack surface analysis. Existing research has developed a variety of reference architectures but none for the specific purpose of attack surface analysis. Existing approaches are either too simple for sufficiently detailed modelling or require too many details to be specified to easily analyse a CAV’s attack surface. Therefore, we propose a reference architecture using a hybrid Functional-Communication viewpoint for attack surface analysis of CAVs, including the Devices, Edge and Cloud systems CAVs interact with. Using two case studies, we demonstrate how attack trees can be used to understand the attack surface of CAV systems.

There is an increasing demand for Internet of Things (IoT) networks consisting of resource-constrained devices executing increasingly complex applications. Due to these resource constraints, IoT devices will not be able to execute expensive tasks. One solution is to offload expensive tasks to resource-rich edge nodes, which requires a framework that facilitates the selection of suitable edge nodes to perform task offloading. Therefore, in this article, we present a novel trust-model-driven system architecture , based on behavioral evidence , that is suitable for resource-constrained IoT devices and supports computation offloading. We demonstrate the viability of the proposed architecture with an example deployment of the Beta Reputation System trust model on real hardware to capture node behaviors. The open environment of edge-based IoT networks means that threats against edge nodes can lead to deviation from expected behavior. Hence, we perform a threat modeling to identify such threats. The proposed system architecture includes threat handling mechanisms that provide security properties such as confidentiality, authentication, and non-repudiation of messages in required scenarios and operate within the resource constraints. We evaluate the efficacy of the threat handling mechanisms and identify future work for the standards used.

Wireless sensor networks (WSNs) are commonly used in asset monitoring applications, where it is often desirable for the location of the asset being monitored to be kept private. The source location privacy (SLP) problem involves protecting the location of a WSN source node from an attacker who is attempting to locate it. Among the most promising approaches to the SLP problem is the use of fake sources, with much existing research demonstrating their efficacy. Despite the effectiveness of the approach, the most effective algorithms providing SLP require network and situational knowledge that makes their deployment impractical in many contexts. In this paper, we develop a novel dynamic fake sources-based algorithm for SLP. We show that the algorithm provides state-of-the-art levels of location privacy under practical operational assumptions.

Source location privacy is becoming an increasingly important property in wireless sensor network applications, such as asset monitoring. The original source location problem is to protect the location of a source in a wireless sensor network from a single distributed eavesdropper attack. Several techniques have been proposed to address the source location problem, where most of these apply some form of traffic analysis and engineering to provide enhanced privacy. One such technique, namely fake sources, has proved to be promising for providing source location privacy. Recent research has concentrated on investigating the efficiency of fake source approaches under various attacker models. In this paper, we (i) provide a novel formalisation of the source location privacy problem, (ii) prove the source location privacy problem to be NP-complete, and (iii) provide a heuristic that yields an optimal level of privacy under appropriate parameterisation. Crucially, the results presented show that fake sources can provide a high, sometimes optimal, level of privacy.