APD-JFAD: Accurate Prevention and Detection of Jelly Fish Attack in MANET

Mobile ad hoc networks (MANETs) are surrounded by tons of different attacks, each with different behavior and aftermaths. One of the serious attacks that affect the normal working of MANETs is DoS attack. A sort of DoS attack is Jellyfish attack, which is quite hard because of its foraging behavior. The Jellyfish attack is regarded as one of the most difficult attack to detect and degrades the overall network performance. In order to combat Jellyfish attack in MANETs, this paper proposes a novel technique called accurate prevention and detection of jelly fish attack detection (APD-JFAD). It is a fusion of authenticated
routing-based framework for detecting attacks and support vector machine (SVM). SVM is utilized for learning packet forwarding behavior. The proposed technique chooses trusted nodes in the network for performing routing of packets on the basis of hierarchical trust evaluation property of nodes. The technique is tested using NS-2 simulator against other existing techniques, i.e., ABC, MABC, and AR-AIDF-GFRS
algorithms by various parameters such as throughput, PDR, dropped packet ratio, and delay. The results prove that APD-JFAD is highly efficient in Jellyfish attack detection and also performs well as compared to other algorithms.