Predictive Maintenance in Leveraging Supervised Machine Learning For Wireless Network Attacks

Authors

  • Vidhya Vivek PG Scholar, Department of A Computer Science Engineering Karpaga Vinayaga College of Engineering & Technology Chinna Kolambakkam, Madhuranthagam(TK), Chengalpattu (DT), PIN 603 308, Tamil Nadu, India Author
  • Dr. J. Jaya Priya Associate Professor, Department of Computer Science Engineering Karpaga Vinayaga College of Engineering & Technology, Chinna Kolambakkam, Madhuranthagam(TK), Chengalpattu (DT), PIN 603 308, Tamil Nadu, India Author

DOI:

https://doi.org/10.32628/IJSRST251368

Keywords:

Predictive Maintenance, Supervised Learning, Wireless Networks, Anomaly Detection, Machine Learning, Network Security, Django Framework

Abstract

Predictive maintenance has emerged as a crucial strategy for ensuring the reliability and efficiency of wireless networks amidst growing cybersecurity threats. This study explores the application of supervised machine learning techniques in predictive maintenance specifically tailored for detecting and mitigating attacks on wireless networks. Leveraging historical network data encompassing various network parameters and security incidents, a predictive model is developed to forecast potential network attacks. The supervised learning approach involves training the model on labeled datasets, where instances of network attacks are explicitly identified. Through feature engineering and selection, relevant network features are extracted to enhance the model's predictive capabilities. The trained model is then deployed to continuously monitor network traffic in real- time, identifying anomalous patterns indicative of potential attacks. Early detection of such threats enables proactive measures to be taken, including network reconfiguration, traffic filtering, and incident response, thus minimizing the impact of cyber attacks and ensuring uninterrupted network operations. The proposed predictive maintenance framework offers a proactive and adaptive approach to network security management, enhancing the resilience of wireless networks against evolving cyber security threats.

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Published

03-08-2025

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Section

Research Articles

How to Cite

Predictive Maintenance in Leveraging Supervised Machine Learning For Wireless Network Attacks. (2025). International Journal of Scientific Research in Science and Technology, 12(4), 872-880. https://doi.org/10.32628/IJSRST251368