The sneeze algorithm: A social network & biomimetic approach for intrusion detection in wireless networks

With organizations around the world increasingly adopting wireless local area networks (WLAN), addressing the security needs of wireless networks becomes a top priority task. Particularly, the issue of "intruders" or the presence of undesired entities, in the network is of utmost importance. Being "friendly", as they usually are, the intruders gain access into the wireless network of an organization, usually without the consent and without the organization's knowledge. This poses a serious security threat since large amounts of important and classified data are exchanged over such networks regularly. Though contemporary tools allow one person to monitor the network constantly to detect such intrusions, the question of human error and judgement arises, and will negate the efficiency of such tools to a certain extent. This paper presents an algorithm called the "sneeze" algorithm to automatically detect wireless network intrusions, and take responsive actions when an intruder is identified. The algorithm also facilitates the identification of an approximate location of the intruder in the network. The work presented in this paper is the starting point of a larger project to implement such a system in an organization to detect the presence of intruders. The novelty of this algorithm is in the fact that it is inspired by the biological "sneezing" process, and that it proposes automatic action in case of intrusion detection, which is not seen in the present day tools.