Data Mining for Anomaly Detection in Network Traffic

Authors

  • Savitha R  Lecturer, Department of Information Science and Engineering, Government Polytechnic, Kalaburagi, Karnataka, India
  • Manjula Moolbharathi  Lecturer, Department of Computer Science and Engineering, Government Polytechnic, Kalaburagi, Karnataka, India

DOI:

https://doi.org/10.32628/IJSRST2269715

Keywords:

Anomaly Detection, Network Traffic, Data Mining, Machine Learning, Intrusion Detection, Feature Selection, Clustering, Classification, Deep Learning, Hybrid Models, False Positives, Real-Time Systems.

Abstract

The paper explores the application of data mining techniques for anomaly detection in network traffic, focusing on enhancing network security through early detection of unusual behavior. Traditional network monitoring methods often struggle with identifying complex, previously unseen attacks, making the adoption of data mining essential. The authors review existing anomaly detection methods, including statistical, machine learning, and hybrid approaches, identifying their limitations. A novel system based on advanced data mining algorithms is proposed, integrating feature selection and preprocessing techniques to improve detection accuracy. The proposed system is evaluated using real-world network traffic datasets, demonstrating significant improvements in detection rates and reduction in false positives. Results are compared to existing methods, showcasing the efficacy of the proposed approach. The paper concludes with an analysis of the system's strengths, its potential for real-time application, and future research directions to further refine anomaly detection systems for evolving network security challenges.

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International Journal of Scientific Research in Science and Technology

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Published

2022-12-30

Issue

Volume 9, Issue 6, November-December-2022

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Savitha R, Manjula Moolbharathi "Data Mining for Anomaly Detection in Network Traffic" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 9, Issue 6, pp.852-868, November-December-2022. Available at doi : https://doi.org/10.32628/IJSRST2269715