Evaluating Machine Learning Algorithms for Network Intrusion Detection Systems: A Comparative Approach
Akhalil Shaikh, Shifa Chilwan, Rehan Shaikh
Vishwakarma University, Pune, India
Abstrct:
Intrusion Detection Systems (IDS) are critical for safeguarding network security by monitoring and analyzing network traffic for suspicious activities. This paper presents a comparative study of three machine learning algorithms—K-Nearest Neighbors (KNN), Logistic Regression, and Decision Tree classifiers—using a publicly available dataset from Kaggle. The objective is to evaluate the performance of these algorithms in detecting network intrusions, comparing their accuracy, precision, recall, F1-score, and computational efficiency. The results highlight the strengths and weaknesses of each model, providing insights into their suitability for real-world IDS applications.
Keywords:
Intrusion Detection System, Artificial Intelligence, Machine Learning, Network Security, Real-time Detection, Threat Detection
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Published on: 06-2024
Journal Name: Science Management Design Journal
Volume: 02
Issue: 02
Pages: 01-11
Month: June
Year: 2024