Machine Learning-Based Intrusion Detection and Prediction in Wireless Sensor Networks for Enhanced Cybersecurity
Wireless Sensor Networks (WSNs) Cybersecurity Intrusion Detection System (IDS) Intrusion Prediction Machine Learning (ML) Supervised Learning Unsupervised Learning Feature Extraction Feature Selection Ensemble Learning Anomaly Detection Sybil Attack Sinkhole Attack Denial-of-Service (DoS) Attack Detection Accuracy False Positive Rate Real-Time Security Predictive Security Secure Network Operations
Wireless Sensor Networks (WSNs) play a crucial role in a wide range of applications, such as industrial automation, smart cities, and healthcare. However, they are susceptible to cyber threats due their distributed and resource-constrained nature. In order to improve WSN cybersecurity, this study proposes an intrusion detection and prediction system based on machine learning (ML). To spot irregularities and anticipate possible security breaches in real time, the suggested solution makes use of both supervised and unsupervised machine learning Techniques for feature extraction and selection are used to increase detection accuracy and decrease the complexity of the data. To merge several models for reliable performance, the system also uses ensemble learning. Model training and evaluation are conducted using an extensive dataset including a range of a
attack types attack types, including Sybil, sinkhole, and denial-of-service (DoS) attacks. The suggested ML-based method delivers excellent Detection accuracy with low false positives rates, and quick response times, according to experimental results. This solution ensures dependable and secure network operations by strengthening WSN security and offering predictive insights to stop future breaches.
"Machine Learning-Based Intrusion Detection and Prediction in Wireless Sensor Networks for Enhanced Cybersecurity ", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.10, Issue 9, page no.b604-b610, September-2025, Available :https://ijsdr.org/papers/IJSDR2509177.pdf
Volume 10
Issue 9,
September-2025
Pages : b604-b610
Paper Reg. ID: IJSDR_305015
Published Paper Id: IJSDR2509177
Downloads: 00055
Research Area: Science and Technology
Country: Belagavi, KARNATAKA, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
