Comparative Study of Machine Learning Algorithms for Intrusion Detection


  • Jyoti Dhanke Bharati Vidyapeeth's College of Engineering, Lavale, Pune 412115, Maharashtra, India
  • R. N. Patil Principal, Department of Mechanical Engineering, Bharati Vidyapeeth's College of Engineering Lavale Pune, India
  • Indra Kumari Korea Institute of Science and Technology Information, KISTI
  • Shiv Gupta IEC college of engineering and technology Greater Noida.
  • Swati Hans Manav Rachna International Institute of Research & Studies, India Department of Computer Science & Engineering
  • Kaushal Kumar Manav Rachna International Institite of Research and Studies Faridabad


Network Traffic Classification, Machine Learning, KNN, SVM


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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How to Cite

Dhanke, J. ., Patil, R. N. ., Kumari, I. ., Gupta, S. ., Hans, S. ., & Kumar, K. . (2023). Comparative Study of Machine Learning Algorithms for Intrusion Detection. International Journal of Intelligent Systems and Applications in Engineering, 12(4s), 647–653. Retrieved from



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