A fuzzy based Q – learning approach to prevent sinkhole attacks in MANET (FQ – SPM)

Abstract

Mobile Ad-Hoc Networks (MANET) are considered one of the significant and growing areas in today’s scenario of technological advancement. It is an infrastructure-less and dynamic ad-hoc network that requires a connection between nodes to deliver packets and data. However, its design adopts a connection-less approach, at the helm of which no monitoring node exists. Hence, the threat of maintaining the network’s security remains an uphill task. Many attacks have been attempted to breach the protection of the MANET. This paper discusses one of the most potent attacks in a MANET infrastructure, the Sinkhole Attack. We try to minimize the possibility of a sinkhole attack using a Fuzzy Q-learning-based approach, a reinforcement learning technique. The results are encouraging, suggesting that sinkhole attacks can be minimized to a great extent after the adaption of the proposed approach.