To detect malicious nodes in the Mobile Ad-hoc Networks using soft computing technique

Abstract

A Mobile Ad-hoc Network (MANET) is a constantly self-configuring, infrastructure-less network of mobile devices where each device is wireless, moves without restraint and be a router to put across traffic unassociated to its own use. Every device must be prepared to constantly sustain the information obligatory for routing the traffic. And this is the main challenge in building a MANET. Such networks may be self operating or linked to a larger internet and may have one or multiple different transceivers between nodes resulting in a highly dynamic and autonomous topology. The first focus is on MANET attacks followed by detection of the malicious node from MANET via Polynomial-Reduction Algorithm. Although scientists have assessed many algorithms for the detection and rectification of the malicious nodes in the MANETs, the problem still persists. Due to the unprecedented growth in technology, the unidentified vulnerabilities are also intensifying. Therefore, it is very crucial to come up with some ground-breaking ideas to prevent the MANET. In this paper we are used NS2 simulator to implementing malicious nodes in MANET.

Hybrid Approach of Intrusion Detection based on Sequential Feature Selection, EM Clustering and Decision Stump Classification

“…According to this paper [14] suggested the fuzzy genetic algorithm that was used in [7,8] may have the higher rate of detection as compare to the decision tree algorithm in most cases, and it was good at detecting unknown attacks. It had a higher detection rate than the traditional genetic algorithm that was used in [6]. The genetic algorithm in [6] had a high detection rate for denial of service attacks.…”

“…It had a higher detection rate than the traditional genetic algorithm that was used in [6]. The genetic algorithm in [6] had a high detection rate for denial of service attacks. When compared with the winning entry of the KDD99 Classifier Learning Contest, it was shown to have a better detection rate for both denial of service and user to root attacks.…”