Intrusion Detection System for Black Hole Detection and Prevention in MANET Using Adaptive Neural Fuzzy Inference Systems
Keywords:
MANETs, ANFIS, Intrusion detection, Black hole Attack, AODVAbstract
Mobile ad hoc network (MANET) is a self-configuring network of mobile nodes formed anytime and anywhere without the help of a fixed infrastructure or centralized management. It has many potential applications in disaster relief operations, military network, and commercial environments. Due to dynamic, infrastructure-less nature, the ad hoc networks are vulnerable to various attacks. AODV is an important on-demand distance vector routing protocol for mobile ad hoc networks. It is more vulnerable to black & gray hole attack. In MANET, black hole is an attack in which a node shows malicious behavior by claiming false RREP (route reply) message to the source node and correspondingly malicious node drops the entire receiving packet. In fuzzy based IDS an intrusion detection system is presented for MANETs against black hole attack detection as well as prevention using fuzzy logic. But it has some issues such as the attack detection accuracy and speed are less, and also it emphasized on very limited features for data collection towards detection of very specific range of attacks. To overcome above issues, the Adaptive Neural Fuzzy Inference Systems (ANFIS) is proposed and detect black hole attack in MANETs. The proposed system will identify the attack over the node as well as provide the solute
on to reduce the data loss over the network. Through simulations, the results prove the proficiency of proposed technique which detect the black hole and improves the network performance.
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