Intrusion Detection in Network Systems Using Machine Learning Algorithms
Keywords:
Intrusion Detection System, Net, Cybersecurity, Network Traffic AnalysisAbstract
Intrusion detection in network systems is a critical component for maintaining cybersecurity and protecting data integrity. This paper explores the application of various machine learning algorithms to identify and classify network intrusions effectively. By leveraging supervised and unsupervised learning techniques, the study aims to enhance detection accuracy while minimizing false positives. Experimental results demonstrate the efficiency of algorithms such as decision trees, support vector machines, and neural networks in analyzing network traffic and detecting malicious activities in real time. The integration of machine learning in intrusion detection systems promises improved adaptability and robustness against evolving cyber threats.
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Copyright (c) 2025 International Journal of Scientific Research in Science, Engineering and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
