Intrusion Detection System Using Machine Learning

This paper presented an intelligent Intrusion Detection System based on the integration of Rough Set Theory (RST) and Support Vector Machine (SVM). RST was employed to reduce data dimensionality by selecting the most relevant network traffic features, while SVM provided accurate classification of normal and malicious activities. Experimental results demonstrated that the proposed RST-SVM model achieved higher detection accuracy, reduced false positive rates, and improved computational efficiency compared to conventional methods such as PCA-SVM. The proposed framework offers an effective and scalable solution for securing modern network environments against evolving cyber threats. Future work may focus on integrating deep learning techniques and real-time threat detection capabilities to further enhance system performance.  

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