Resource-Aware CNN-IIDS for Intrusion Detection in WSNs with Multi-Dataset Evaluation

Sumedh Dhengre; Shabnam Sayyad

doi:10.48084/etasr.19346

Authors

Sumedh Dhengre Department of Computer Engineering, AISSMS College of Engineering, Savitribai Phule Pune University, Pune, India
Shabnam Sayyad Department of Computer Engineering, AISSMS College of Engineering, Savitribai Phule Pune University, Pune, India

Volume: 16 | Issue: 4 | Pages: 37360-37368 | August 2026 | https://doi.org/10.48084/etasr.19346

Received: 16 April 2026 | Revised: 6 May 2026, 21 May 2026, and 23 May 2026 | Accepted: 24 May 2026 | Online: 10 June 2026

Corresponding author: Sumedh Dhengre

Abstract

Wireless Sensor Networks (WSNs) are vulnerable to cyber-attacks due to their limited energy and computational resources. This study proposes a resource-aware Convolutional Neural Network-based Intelligent Intrusion Detection System (CNN-IIDS) for efficient multi-attack detection in WSN environments. The framework integrates hybrid feature engineering, combining traffic-based features with node-level resource metrics. The framework employs a lightweight CNN for automated feature learning and classification. The proposed system detects multiple attacks, including Denial of Service (DoS), User-to-Root (U2R), Remote-to-Local (R2L), and Wormhole attacks. To ensure robustness, the model is evaluated on multiple datasets, including simulation-based and benchmark datasets such as WSN-DS, and is compared with machine learning classifiers, including Random Forest. The experimental results demonstrate high detection accuracy, precision, recall, and low false positive rates. The proposed CNN-IIDS achieves an effective balance between performance and computational efficiency, making it suitable for resource-constrained WSN environments.

Keywords:

Wireless Sensor Networks (WSNs), Intrusion Detection System (IDS), Convolutional Neural Network (CNN), resource-aware model, multi-attack detection, multi-dataset evaluation, network security

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