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@article{183717,
        author = {SAMPATH KUMAR S and santhosh SG and Zakiya Farheen},
        title = {Smart Intrusion Prevention System Using Integrated Machine Learning Models (Random Forest Classifier with Flask)},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {2872-2877},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183717},
        abstract = {As cyber threats evolve, modern networks become more vulnerable to sophisticated attacks, particularly advanced persistent threats and covert intrusions. Conventional intrusion detection systems usually fail to respond quickly enough or adapt to fresh assault patterns. This study describes a Smart Intrusion Prevention System (SIPS) that uses machine learning, namely the Random Forest Classifier, to detect and block intrusions in real time. In security circumstances, the system is implemented with a lightweight Flask web interface to improve usability and speed. Following training with features from a benchmark intrusion dataset, the model distinguishes between malicious and regular network traffic. Criteria For Evaluation parameters including accuracy, precision, recall, and F1-score are analyzed to show how well the system recognizes threats. The suggested framework offers businesses seeking to improve using machine learning to enhance network security a workable and expandable solution.},
        keywords = {Cyber-attack detection, Flask web framework, Intrusion prevention system (IPS), Network security, Random Forest classifier, and Real-time threat mitigation.},
        month = {October},
        }