Wireless Sensor Networks (WSNs) are highly vulnerable to security
threats due to their decentralized nature, constrained resources, and
open communication channels. Traditional intrusion detection and
prevention systems (IDPS) often struggle to provide real-time
protection while maintaining network efficiency. The increasing
complexity of cyberattacks necessitates advanced techniques for threat
mitigation. A major challenge in WSN security is the detection of
sophisticated intrusions with high accuracy while minimizing false
positives and computational overhead. Conventional rule-based and
anomaly-based detection methods exhibit limitations in identifying
emerging threats due to their reliance on predefined signatures and
static models. Addressing these gaps, a hybrid deep learning-based
IDPS is proposed, integrating Convolutional Neural Networks (CNNs)
for feature extraction and Long Short-Term Memory (LSTM) networks
for sequential pattern learning. The hybrid model is trained on a
benchmark WSN intrusion dataset and optimized using the Adam
optimizer to enhance detection performance. Experimental evaluation
shows that the proposed model achieves an intrusion detection
accuracy of 98.6%, significantly outperforming traditional machine
learning approaches such as Support Vector Machines (SVM) (91.2%)
and Random Forest (94.8%). The system also reduces false positive
rates to 1.8%, ensuring reliable threat identification. Moreover, real-
time implementation exhibits an average detection latency of 0.35
seconds, making it suitable for resource-constrained WSN
environments. These results indicate that the hybrid CNN-LSTM model
effectively enhances the security of WSNs, providing a robust defense
against evolving cyber threats.
V. Balajishanmugam1, A. Christopher Paul2, B. Thirunavukarasu3
PPG Institute of Technology, India1, Karpagam Institute of Technology, India2, Queensland University of Technology, Australia3
Intrusion Detection, Wireless Sensor Networks, Deep Learning, Cybersecurity, Threat Mitigation
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| Published By :
ICTACT
Published In :
ICTACT Journal on Communication Technology
( Volume: 16 , Issue: 1 , Pages: 3454 - 3458 )
Date of Publication :
March 2025
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28
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