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@article{177691,
        author = {SAVITHA J and SANKARI A and MINI BALA G and ANBU RAJA G and PONNEELA VIGNESH R},
        title = {Network Threat Hunting and Detection System using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {1430-1434},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177691},
        abstract = {In the modern digital landscape, traditional intrusion detection systems (IDS) are increasingly inadequate to address sophisticated cyber threats. This study introduces a Network Threat Hunting and Detection System powered by machine learning (ML) to address the existing challenges. Using the KDD Cup 1999 dataset, the study explores various ML models, including Logistic Regression, Decision Tree, Random Forest, Naive Bayes, K-Nearest Neighbours (KNN), and Support Vector Machine (SVM). Data preprocessing techniques such as normalization, encoding, and feature selection were employed to enhance model performance. Among the models evaluated, the Random Forest classifier demonstrated superior results, achieving an accuracy of over 99%. This system effectively distinguishes between normal and malicious network traffic, offering a scalable, adaptive, and highly accurate approach to intrusion detection. Future enhancements include integrating real-time detection, deploying deep learning models, and using explainable AI for greater transparency.},
        keywords = {Network Security, Intrusion Detection System, Machine Learning, Random Forest, Cybersecurity.},
        month = {May},
        }