A quantitative approach towards detection of an optimal attack path in a wireless network using modified PSO technique

In recent years there has been an immense proliferation of wireless networks and they are becoming increasingly vulnerable to attacks. Thus there is a serious need to secure such networks from attacks. Usually an attacker can penetrate into a network by utilizing a chain of exploits. An exploit is a small piece of code that makes use of vulnerabilities present in a service or in a system. Each exploit in the chain has a set of preconditions and effects and lays the groundwork for the subsequent exploits. Application of such a chain of exploits generates a set of attack states or network states which form a path called the attack path and combining many such attack paths produces an attack graph. A lot of research has been done on issues such as scalable and time efficient ways of generation of attack graphs in wired network in contrast to that in wireless scenario. Moreover, the need is to identify the path that may be chosen by the attacker to comprise a target system in less time and effort. The proposed methodology in this paper aims as finding out the optimal or risk-prone attack path that the attacker may choose to penetrate a wireless network. The generation of attack paths in a wireless network is itself a difficult proposition due to networks inherent dynamic nature and ever changing topology. In this work, the Particle Swarm Optimization (PSO) technique has been employed for finding out the optimal attack path using an attack vector metric. The effort required on the part of the attacker to compromise a target system has been termed as an attack vector. The wireless nodes have been assigned severity measures obtained from customized risk parameters which serve as an input to the modified PSO technique. A case study has also been presented to demonstrate the efficacy of the proposed methodology.