A Framework for Various Attack Identification in MANET Using Multi-Granular Rough Set

A Framework for Various Attack Identification in MANET Using Multi-Granular Rough Set

N. Syed Siraj Ahmed (VIT University, Vellore, India) and Debi Prasanna Acharjya (School of Computing Sciences and Engineering,VIT University, Vellore, India)
Copyright: © 2019 |Pages: 25
DOI: 10.4018/IJISP.2019100103

Abstract

The topology changes randomly and dynamically in a mobile adhoc network (MANET). The composite characteristics of MANETs makes it exposed to interior and exterior attacks. Avoidance support techniques like authentication and encryption are appropriate to prevent attacks in MANETs. Thus, an authoritative intrusion detection model is required to prevent from attacks. These attacks can be at either the layers present in the network or can be of a general attack. Many models have been developed for the detection of intrusion and detection. These models aim at any one of the layer present in the network. Therefore, effort has been made to consider either the layers for the detection of intrusion and detection. This article uses a multigranular rough set (MGRS) for the detection of intrusion and detection in MANET. The advantage of MGRS is that it can aim at either the layers present in the network simultaneously by using multiple equivalence relations on the universe. The proposed model is compared with many traditional models and attained higher accuracy.
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1. Introduction

Increasing popularity of wireless equipment and devices has inclined to study self-configuring and self-healing networks with exclusion of centralized or pre-established environment. Such a network having no pre-established or centralized environment is called ad hoc network in which nodes employ multi hop links to communicate information between each other. The communication between nodes is carried out through intermediate nodes. These intermediate nodes act as a router in a static way whereas nodes of mobile ad hoc network, operates independently and connected through wireless associations in a dynamic way. Nodes in a mobile ad hoc network create lived short term or temporary network in which it can move freely and randomly. At the same time nodes can act as a router participating in maintaining and determining the paths of other nodes. But, the wireless links between the nodes are deeply vulnerable to attack with a chance of losing associations promptly due to mobility. Thus, in mobile ad hoc network, nodes behave like self-managing routers connected through wireless associations. Each router in mobile ad hoc network is selfish, free to decision making, and move arbitrarily. Therefore, such type of network able to operate separately as well as associated with superior network such as the internet. Additionally, such kind of network is very simple to connect, configure, and quick to use abruptly such as ad hoc meeting, ad hoc classroom, etc. The rise in use of this network needs novel protection system and strategies to assure accessibility, reliability, and attack free environment for effective communication. To maintain attack free and user-friendly environment in mobile ad hoc network, novel detection and attack prevention system unlike the intrusion detection system of wired network is essential for further research.

Over the preceding few years various attacks in mobile ad hoc network discussed are denial of service, jamming, sybil, flooding, black hole, worm hole, jelly fish, link spoofing, malicious message injecting, eaves dropping, etc. (Shrivastava & Jain, 2013; Wazid, Singh & Goudar, 2011; Ahmed & Acharjya, 2015a, 2015b). A scheme to control fake flooding route requests in ad hoc network is also discussed (Kataria, Dhekne &. Sanyal, 2006). Discovering of anomalous such as black hole, resource consumption, and packet dropping in mobile ad hoc network was addressed by Fattah, Dahalin & Jusoh (2010) using nearest neighbour and distance outlier. Similarly, identification of denial of service attack in MANET using principal component analysis with features reduction was also proposed. This model uses dynamic source routing protocol to recognize denial of service attacks on different wireless network traffics (Kabiri & Aghaei, 2011).

Later, Chouhan & Yadav (2011) discussed about ad hoc flooding attack detection and prevention in MANET by using ad hoc on demand distance vector (AODV) protocol and dynamic source routing (DSR). The major limitation of this model is that it takes extra bit of time for the detection of misbehaving nodes. Similarly, Pastrana, Mitrokotsa, Orfila and Lopez (2012) portrayed an assessment to detect intrusion in MANET by using various classifiers such as support vector machine, genetic programming, multi-layer perceptron, linear classifier, naive Bayes, and Gaussian mixture model. The efficiency assessment identified that genetic programming model was more suitable to detect intruder, whereas support vector machine classifier model was suitable for identification of distinct attacks such as packet dropping, flooding, black hole and forging. Mitrokotsa, Tsagkaris and Douligeris (2008) projected supervised learning classification algorithms such as support vector machine, multi- layer perceptron, Gaussian mixture model, and naive Bayes classification for detection of various attacks like packet dropping, black hole, flooding, and forging. It is observed that classification using support vector machine generates maximum accuracy (92%) for all attacks.

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