Detecting misbehaving nodes in MANET with an artificial immune system based on type-2 fuzzy sets

Last decade has witnessed an enormous growth in wireless networks that naturally has brought some new research challenges. Related studies conducted have covered several research areas like routing protocols, encrypted authentication protocols, misbehavior detection system and a number of innovative solutions, biologically-inspired and not, have been suggested to several open problems. In this position paper, we present a biologically-inspired type-2 fuzzy set recognition algorithm for detecting misbehaving nodes in an ad-hoc wireless network. This work investigates the possibility of detecting misbehaving nodes, learning bad behaviors, protecting the network from reinfection and mitigating the problem of routing misbehavior without human intervention, exploiting biological techniques evolved over millions of years. In order to protect the system of unwanted behaviors and take under control the number of false positive, our solution mimics the binding process between lymphocytes receptors of the immune cells and antigens.