Title: Deep neural network for the purpose of developing an intrusion detection system for wireless sensor networks

Authors: Swagata Sarkar; B.V. Santhosh Krishna; D. Chithra; Sheshang Degadwala

Addresses: Artificial intelligence and Data Science Department, Sri Sairam Engineering College, West Tambaram, Chennai, India ' Computer Science and Engineering, New Horizon College of Engineering, Bengaluru, Karnataka, India ' Department of Information Technology, S.A. Engineering College, Chennai – 77, India ' Department of Computer Engineering, Sigma University, Vadodara, Gujarat, India

Abstract: A wireless sensor network is composed of a large number of sensor nodes, which collects data and transmits it to a centralised location. They have a lot of security problems, though, because nodes have limited resources. Network intruder monitoring systems do these things for the network, and any information network has to have them. Techniques from the field of machine learning are often used in breach detection systems. Based on the findings of this research, a deep neural network-based intruder detection system was proposed as a solution to this issue and an improvement to performance. Intrusion detection systems can be very helpful in finding and stopping security threats. One way to solve this problem is to use the intrusion detection system with more efficient methods that can choose the best route at every point. The proposed method shows an accuracy of 97.5%.

Keywords: deep neural networks; DNN; deep learning; DL; wireless sensor networks; WSN; base station; BS; intrusion detection system; IDS.

DOI: 10.1504/IJESDF.2025.147191

International Journal of Electronic Security and Digital Forensics, 2025 Vol.17 No.4, pp.510 - 521

Received: 25 Sep 2023
Accepted: 21 Dec 2023
Published online: 11 Jul 2025 *

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