Data Mining for Anomaly Detection in Network Traffic
Savitha R, Manjula Moolbharathi · International Journal of Scientific Research in Science and Technology · 2022
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.