Comparative Study of Machine Learning Algorithms for Intrusion Detection
Keywords:
Network Traffic Classification, Machine Learning, KNN, SVMAbstract
Researching Network Traffic Classification through Machine Learning is crucial given the expanding reach of the internet, enabling global information exchange. The implications of security breaches extend beyond individuals to impact entire organizations. Hence, discerning between malicious and non-malicious data on the network holds utmost significance. In this research, we perform an in-depth examination and contrast of seven distinct machine learning algorithms: Naïve Bayes, Logistic Regression, Support Vector Machine (SVM), Random Forest, C4.5, XGBoost, and k-Nearest Neighbors (KNN). These analyses are executed using Python's package module for seamless programmatic execution. The assessment encompasses metrics such as accuracy, precision, and recall, offering valuable insights into the performance of each algorithm.
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