Regression-based Link Failure Prediction with Fuzzy-based Hybrid Blackhole/Grayhole Attack Detection Technique

摘要

Most of the conventional routing protocols in Mobile Ad-hoc Network (MANET) utilize the path until a link breaks or failures. During the path reconstruction, packets may be lost which can cause significant packet delivery ratio and throughput degradation. The previous researches such as FMQTMHDCR approaches were developed for only detecting the blackhole and grayhole attacks which allows the prediction of malicious nodes in the network. However, the prediction of link failures was not considered which may degrade the network performance. Hence in this article, a novel Link Failure Prediction (LFP) algorithm is integrated to the Dynamic Source Routing (DSR) protocol. In this approach, a linear regression model is applied on the Received Signal Strength (RSS) of each node for predicting the link failure time. Once the link failure time is predicted, a warning is transmitted to the source node if the link is soon-to-be-broken or failure. Then, the source node can reconstruct the new route before the links failure time. The simulation results show that the proposed mechanism known as FMQTMHDCR-LFP can significantly increases the packet delivery ratio, throughput, normalized routing overhead and reduces the packet drop rate. Also, this approach prevents the blackhole and grayhole attacks in the network.