Using Machine Learning Techniques to Simulate Network Intrusion Detection

This article explores the application of machine learning methods for modeling intrusion detection systems. We present the research findings, including the construction of a model based on decision trees and the analysis of significant factors determining network attacks. The results demonstrate high model accuracy and the potential for its use in enhancing network security. The article also delves into the specifics of the chosen dataset and data preprocessing methods used to ensure model accuracy. Additionally, possibilities for future research in the field of intrusion detection are discussed, including the utilization of other machine learning methods, analysis of new threat types, and the study of attack obfuscation methods. This study offers important practical and theoretical recommendations for information security professionals and machine learning researchers.

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