Computational Security for Context-Awareness in Vehicular Ad-Hoc Networks

Abstract

Vehicular ad hoc networks can be viewed as a typical context-aware system where the experienced context frequently varies as vehicles move along road, e.g., signal-to-noise ratio (SNR), velocity, and traffic flow. In particular, the adopted security protection mechanisms often depend on the node state, location, and/or surrounding risk, which need the capability of context-aware security quantification. This paper views the security level as a user's inherent property that is only correlated with the user's behaviours and the situated context and independent of the suffered attack ways. We propose a formalized methodology to especially quantify the security level in real time from the perspective of state transition probability through estimating the stable probability of staying in the security state in inhomogeneous continuous time Markov chain. This paradigm enables users to customize the security protection mechanisms for adapting to the frequently varying context. We conduct the extensive numerical calculations and empirical analysis to comprehensively investigate the response of the proposed security quantification framework to the various combinations of the concerned parameters, e.g., SNR, velocity, and traffic flow. The results show that the proposed framework is capable of capturing the real-time security level adaptively to the vehicular context and provides a dependable decision basis to security protection, which can restrict the security to a target value.