Optimal Machine Learning Based Intrusion Detection System in Wireless Sensor Networks for Surveillance Applications

Authors

  • Sibi Amaran Department of Computer Science and Engineering, Annamalai University, Chidambaram, India
  • Ramalingam Madhan Mohan Department of Computer Science and Engineering, Annamalai University, Chidambaram, India
  • Rethnaraj Jebakumar Department of CSE, SRM Institute of Science and Technology, Kattankulathur, Chennai, India https://orcid.org/0000-0002-1570-2561

DOI:

https://doi.org/10.13052/jmm1550-4646.1924

Keywords:

Intrusions, security, WSN, machine learning, K-means clustering, parameter tuning

Abstract

Security is considered as a major design issue in wireless sensor network (WSN) and can be solved by the use of intrusion detection systems (IDS). In this view, this paper devises a new k-means clustering with optimal support vector (KM-OSVM) based IDS for WSN. The KM-OSVM model incorporates preprocessing, clustering, classification, and parameter tuning. Primarily, data preprocessing and K-means clustering technique are applied to group the data instances into a set of clusters. Besides, SVM based classification technique is employed to allot class labels, and the parameters in SVM are optimally adjusted by the use of crow search optimization (CSO) algorithm, shows the novelty of the work. The experimental outcome of the KM-OSVM model is examined using UNSW-NB15 and CICIDS2017 datasets. The obtained outcomes demonstrated that the KM-OSVM model ensured better performance with the maximum accuracy of 95.12% and 98.98% respectively. Therefore, the KM-OSVM model can be employed as an effective tool to achieve security in the resource constrained WSN.

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Author Biographies

Sibi Amaran, Department of Computer Science and Engineering, Annamalai University, Chidambaram, India

Sibi Amaran, Research Scholar in the Department of COMPUTER SCIENCE & ENGG. in Annamalai University for 4 Years and research area is Wireless Sensor Networks.

Ramalingam Madhan Mohan, Department of Computer Science and Engineering, Annamalai University, Chidambaram, India

Ramalingam Madhan Mohan, working as Associate Professor in the Department of COMPUTER SCIENCE & ENGG. in Annamalai University. My Teaching and research Experience is 22 years and research area is Wireless Sensor Networks.

Rethnaraj Jebakumar, Department of CSE, SRM Institute of Science and Technology, Kattankulathur, Chennai, India

Rethnaraj Jebakumar, working as Associate Professor in the Department of Computing Technologies in SRM Institute of Science and Technology. My Teaching and Research Experience is 18 years and research area is Wireless Sensor Networks, Mobile Ad hoc Networks, Cloud Computing, Big Data, Data Mining and IOT.

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https://research.unsw.edu.au/projects/unsw-nb15-dataset

https://www.unb.ca/cic/datasets/ids-2017.html

Published

2022-11-15

Issue

Section

New Trends in Real-Time Image and Video Processing for Surveillance and Security