AN ENSEMBLE MACHINE LEARNING–BASED INTRUSION DETECTION FRAMEWORK FOR NETWORK SECURITY USING THE CICIDS-2017 DATASET
Keywords:
Network Security, Intrusion Detection System(IDS), Random Forest, XGBoost, Machine Learning, CICIDS-2017Abstract
The escalating frequency and growing sophistication of cyberattacks have placed modernnetwork infrastructures under relentless strain. Conventional signature-based intrusiondetection systems (IDS) are limited in their ability to identify novel and evolving threats, motivating the adoption of machine learning-based detection techniques.
References
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
Buczak, A. L., & Guven, E. (2016). A survey of data mining and machine learning
methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials, 18(2), 1153–1176.
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Published
2024-12-15
How to Cite
Ayush Gupta, Abhay Shukla, Somendra tripathi. (2024). AN ENSEMBLE MACHINE LEARNING–BASED INTRUSION DETECTION FRAMEWORK FOR NETWORK SECURITY USING THE CICIDS-2017 DATASET . Journal of Computational Analysis and Applications (JoCAAA), 33(08), 8304–8314. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5297
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