Self-organized classification of dangers for secure wireless mesh networks

This paper introduces danger theory in Artificial Immune System as a method of responding to danger in wireless mesh networks. It identifies the challenges in deploying Wireless Mesh Networks (WMNs) and focus on secure routing as one of the key challenges in deploying WMNs. In order to implement a secure routing system, various Artificial Immune System (AIS) models were analysed. These models have been used in Intrusion Detection System (IDS) and computer security in the literature. In this paper, the authors propose to use Danger models to secure routing in WMNs. The first step in secure routing process is to identify and classify the network dangers and take necessary actions to overcome those dangers. For the classification task, we apply Self-organizing Maps (SOMs) as the classifier to classify the danger levels in WMNs. These identified danger conditions are further deployed as the warning signals for the design of secure routing protocol. The experimental results validate the proposal of applying the Danger Theory (DT) into security area and good performance is also reported by the use of Artificial Neural Network (ANN) classifier.