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A Quasi-Newton Neural Network Based Efficient Intrusion Detection System for Wireless Sensor Network

A. Gautami1,*, J. Shanthini2, S. Karthik3
1 Department of Electronics and Communication Engineering, SNS College of Technology, Coimbatore, Tamilnadu, India
2 Department of Computer Science & Engineering, Dr. N.G.P Institute of Technology, Coimbatore, Tamilnadu, India
3 Department of Computer Science & Engineering, SNS College of Technology, Coimbatore, Tamilnadu, India
* Corresponding Author: A. Gautami. Email:

Computer Systems Science and Engineering 2023, 45(1), 427-443.

Received 02 January 2022; Accepted 18 April 2022; Issue published 16 August 2022


In Wireless Sensor Networks (WSN), attacks mostly aim in limiting or eliminating the capability of the network to do its normal function. Detecting this misbehaviour is a demanding issue. And so far the prevailing research methods show poor performance. AQN3 centred efficient Intrusion Detection Systems (IDS) is proposed in WSN to ameliorate the performance. The proposed system encompasses Data Gathering (DG) in WSN as well as Intrusion Detection (ID) phases. In DG, the Sensor Nodes (SN) is formed as clusters in the WSN and the Distance-based Fruit Fly Fuzzy c-means (DFFF) algorithm chooses the Cluster Head (CH). Then, the data is amassed by the discovered path. Next, it is tested with the trained IDS. The IDS encompasses ‘3’ steps: pre-processing, matrix reduction, and classification. In pre-processing, the data is organized in a clear format. Then, attributes are presented on the matrix format and the ELDA (entropy-based linear discriminant analysis) lessens the matrix values. Next, the output as of the matrix reduction is inputted to the QN3 classifier, which classifies the denial-of-services (DoS), Remotes to Local (R2L), Users to Root (U2R), and probes into attacked or Normal data. In an experimental estimation, the proposed algorithm’s performance is contrasted with the prevailing algorithms. The proposed work attains an enhanced outcome than the prevailing methods.


Distance fruit fly fuzzy c-means (DFFF); entropy-based linear discriminant analysis (ELDA); Quasi-Newton neural network (QN3); remote to local (R2L); denial of service (DoS); user to root (U2R)

Cite This Article

A. Gautami, J. Shanthini and S. Karthik, "A quasi-newton neural network based efficient intrusion detection system for wireless sensor network," Computer Systems Science and Engineering, vol. 45, no.1, pp. 427–443, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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