An Efficient Data Driven Model for WSN using Artificial Neural Network

Vivek Kumar Pandey, Shiv Prakash, Gaurav Ojha, Tiansheng Yang, Rajkumar Singh Rathore

Research output: Contribution to journalConference articlepeer-review

Abstract

Modern communication systems rely on WSNs as the most effective medium for remote monitoring and data acquisition across different environments. However, their inherent power constraints make providing security while maintaining efficiency in WSN nodes challenging. The existing solutions in the literature often fail to strike the best possible balance between security effectiveness and resource utilization. This paper proposes a data-driven intrusion detection system using a neural-network architecture optimized for a resource-constrained WSN environment. Based on the benchmark NSL-KDD dataset, our model uses medium-sized neural networks with activation functions and hidden layers to process patterns in the network traffic equally well. The study finds that our ANN-based approach outperforms the current techniques with 98.62% accuracy and a minimal computational cost of 2,053 units. This outcome surpassed all the comparable methods, including Gaussian SVM, which attained 98.52% accuracy with a cost of 2,194 units, and Decision Tree at a cost of 5,731 units, with an accuracy of 97.83% respectively.
Original languageEnglish
Pages (from-to)1446-1453
Number of pages8
JournalProcedia Computer Science
Volume258
Early online date10 May 2025
DOIs
Publication statusPublished - 10 May 2025
Event3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024 - Dehradun, India
Duration: 28 Nov 202429 Nov 2024

Keywords

  • Artificial Neural Network
  • Intrusion Detection System
  • Security
  • WSN

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