Technical Journal, Vol. 16 No. 1, 2022.
Original scientific paper
https://doi.org/10.31803//tg-20210603125428
Salient Features Selection Techniques for Instruction Detection in Mobile Ad Hoc Networks
Severin Gbetondji Michoagan
; Department of Computer Science, Savitribai Phule Pune University, Ganeshkhind Road, Pune - 411 007, Maharashtra State, India
S. M. Mali
; (1) Department of Computer Science, Savitribai Phule Pune University, Ganeshkhind Road, Pune - 411 007, Maharashtra State, India (2) Dr. Vishwanath Karad, MIT World Peace University, Ground Floor, Saraswati Vishwa A, Mitwpu Campus, S.No.124, Paud Road, Kothrud, Pune - 411038, Maharashtra State, India
Sharad Gore
; Department of Computer Science, Savitribai Phule Pune University, Ganeshkhind Road, Pune - 411 007, Maharashtra State, India
Abstract
The development of wireless mobile ad hoc networks offers the promise of flexibility, low cost solution for the area where there is difficulties for infrastructure network. A key attraction of this mode of communication is their ease of deployment and operation. However, having a good and robust mobile ad hoc networking will depend entirely on security mechanism system in place. Traditional security mechanisms know as firewalls were used for defensive approach to oppose security obstacle. However, firewalls do not fully or completely defeat intrusions. To cope with this limitation, various intrusions detection systems (IDSs) have been proposed to detect such network intrusion activities. The problem encounter for this particular technique of instruction detections technique is that during network monitoring for data collection for anomaly detection, data that does not contribute to detection must be deleted before detection can be processed or application of learning algorithm for detection of abnormal attacks. In this paper we present a novel feature technique for feature selection before learning technique should be applied. The method has been applied into our own data set, and for the detection purpose we have used most of the well reputed three Machine Learning classifiers with the new selected features for performance evaluation and the experiment shows that higher accuracy results could be achieved with only all the 9 features extracted with our own algorithm with the data set created by using RandomForest classifier.
Keywords
AODV; IDS; Jammer Node; MANET; OPNET; Spoofing attack; Wormhole attack
Hrčak ID:
271925
URI
Publication date:
4.2.2022.
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