Adaptive Association Rule Mining Based Cross Layer Intrusion Detection System for MANET
Mobile Ad-hoc wireless NETworks (MANET) are a significant area of research with many applications. MANETs are more vulnerable to malicious attack. Authentication and encryption techniques can be used as the first line of defense for reducing the possibilities of attacks. Alternatively, these approaches have several demerits and designed for a set of well known attacks. This paper proposes a cross layer intrusion detection architecture to discover the malicious nodes and different types of DoS attacks by exploiting the information available across different layers of protocol stack in order to improve the accuracy of detection.