JISE


  [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14]


Journal of Information Science and Engineering, Vol. 39 No. 6, pp. 1233-1246


Ensemble Deep Learning Classifier with Optimized Cluster Head Selection for NIDS in MANET


V. GOKULA KRISHNAN1, P. A. ABDUL SALEEM2, N. KIRUBAKARAN3, VEERAMALAI
SANKARADASS4, ATA. KISHORE KUMAR5, C. JEHAN6, J. DEEPA7 AND G. DHANALAKSHMI8
1Department of CSE, Saveetha School of Engineering
Saveetha Institute of Medical and Technical Sciences
Tamil Nadu, 602105 India

2Department of CSE (DS), CVR College of Engineering
Mangalpally, Hyderabad, Telangana, 501510 India

3,4,6Department of CSE, Chennai Institute of Technology
Tamil Nadu, 600069 India

5Department of ECE, Sree Vidyanikethan Engineering College
Mohan Babu University
Tirupati, 517102 India

7Department of CSE, Easwari Engineering College
Tamil Nadu, 600089 India

8Department of IT, Panimalar Engineering College
Tamil Nadu, 600123 India
E-mail: gokul_kris143@yahoo.com
1; drsaleemprincipal@gmail.com2;
Iamkiru70@gmail.com
3; veera2000uk@gmail.com4; drkishore1609@gmail.com5; cjehan2001@gmail.com6; deepa.j@eec.srmrmp.edu.in7; dhanalakshmi4481@gmail.com8


A MANET security is more fragile and susceptible to the environment due to the lack of a centralized environment for monitoring the behavior of individual nodes during com-munication in this type of network. Both local and global invaders are able to access the networks they target. In MANETs, where nodes can move in any direction and topology is constantly changing, node mobility and node energy are two critical optimization chal-lenges. As a result, remote monitoring of node performance and behavior is employed by Network Intrusion Detection Systems (NIDSs) as a solution to cope with the problem of intrusion into these networks. The proposed method is used to develop a Cuttlefish Algo-rithm with Ensemble Deep Learning Classifier (CFA-EDL) for multi-attack intrusion de-tection. A clustering algorithm for MANET cluster head election is developed in this re-search by focusing on the challenges of mobility and energy. To select the cluster head, the CFA uses the EDL Classifier, while the EDL Classifier identifies several attacks. Mul tiple attacks are identified using EDL Classifier. Extensive testing in MATLAB and com-parisons with other existing methods are included in the planned research. Attack detection, memory ingesting and computing time for classifying an intruder are some of the metrics used to evaluate the suggested method’s performance. The results of the simulation show that the suggested strategy significantly reduces IDS traffic and memory ingesting while maintaining an attack detection rate in the shortest amount of time possible.


Keywords: mobile ad hoc networks, security, attack detection, cuttle fish algorithm, deep learning classifier, network intrusion detection systems

  Retrieve PDF document (JISE_202306_03.pdf)