Research On The Security For Context-awareness And Security Driven Modeling Of Networks

Vehicular ad-hoc networks differ from the wired networks and behave in a highly dynamic context, e.g. frequently changing signal-to-noise ratio(SNR) and security risks, which undoubtedly affects the experienced quality-of-service(Qo S) and security. In this paper, we propose to dynamically balance the anticipated Qo S and security for adapting to the varying vehicular context and the server applications with aims to attain a satisfactory performance rating but without compromising any security. To this end, a variant of IKEv2 called Vehicular Internet Key Exchange(VIKE) is put forward to autonomously negotiate the optimal encryption and integrity algorithms and the related profile that particularly suit to the current context with respect to the confronted SNR, security risk and application requirements. We theoretically derive the relations between the Qo S and security for analytical solutions in terms of four categories of vehicular applications. The extensive numerical calculations are conducted to comprehensively investigate how the proposed VIKE responses to the various combinations of the SNR, modulation scheme and key length. The results show that the VIKE is capable of self-adapting to the vehicular context, and of contributing to the quality of communication performance without compromising any security. The proposed VIKE is expected to port the mass-deployed IKE into securing the emerging numerous vehicular applications and services.Network modeling is a flexible mathematical structure that is able to identify statistical regularities and structural principles common to complex systems. The majority of recent driving forces in modeling complex networks are originated from activity, in which an activity potential of a time invariant function is introduced to identify agents’ interactions and to construct an activity-driven model. However, the new-emerging network evolutions are already deeply coupled with not only the explicit factors(e.g. activity) but also the implicit considerations(e.g. security and trust), so more intrinsic driving forces behind should be integrated into the modeling of time varying networks. The agents undoubtedly seek to build a time-dependent trade-off among activity, security, and trust in generating a new connection to another. Thus, we reasonably propose the Activity-Security-Trust(AST) driven model through synthetically considering the explicit and implicit driving forces(e.g. activity, security, and trust)underlying the decision process. AST-driven model facilitates to more accurately capture highly dynamical network behaviors and figure out the complex evolution process, allowing a profound understanding of the effects of security and trust in driving network evolution, and improving the biases induced by only involving activity representations in analyzing the dynamical processes.