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@article{177311,
        author = {Sai Venkat and K.Rahul and T.Rahul and Ch.Lohith and Ch.Meghana},
        title = {Intrusion Prevention System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {8306-8310},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177311},
        abstract = {Intrusion Prevention Systems (IPS) are critical components of network security that monitor and proactively block potential threats before they can compromise a system. This work focuses on building a machine learning-based IPS capable of preventing network attacks in real time. The CIC-IDS2017 dataset, a widely used benchmark for network intrusion prevention, is utilized. Three machine learning algorithms—Support Vector Machines (SVM) for binary classification, and Random Forest and K-Nearest Neighbors (KNN) for attack type classification—are implemented and evaluated using key metrics, including accuracy, precision, recall, and F1-score. Results show that Random Forest outperforms other models in multi-class classification, while SVM achieves high accuracy in intrusion detection. The findings highlight the potential of machine learning-based IPS in enhancing cybersecurity by reducing false positives and improving real-time threat mitigation.},
        keywords = {Intrusion Prevention System, Machine Learning, Cybersecurity, CIC-IDS2017, Random Forest, SVM, KNN, Network Security},
        month = {May},
        }