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@article{177195,
        author = {Devika G and Manasa S P and Sudeep K and Shariya Habiba K S and Shivanand Mirji},
        title = {FEATURE ANALYSIS AND RANDOM FOREST CLASSIFICATION FOR NETWORK INTRUSION DETECTION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {1073-1079},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177195},
        abstract = {The increasing frequency and sophistication of cyberattacks have highlighted the critical need for intelligent and adaptive Intrusion Detection Systems (IDS). This project presents the design and implementation of a machine learning-based IDS utilizing the Random Forest algorithm to detect various types of network intrusions with high accuracy and efficiency. The system is trained and tested using benchmark datasets such as NSL-KDD, enabling it to learn and classify patterns of normal and malicious activities. Data preprocessing steps, including feature selection, normalization, and data balancing, were applied to improve model performance. The Random Forest classifier was chosen for its robustness, interpretability, and ability to handle complex and high-dimensional data. Experimental results demonstrate that the proposed system achieves high detection rates and low false positive rates, outperforming several traditional and contemporary machine learning approaches. This project contributes to the development of scalable and effective intrusion detection solutions suitable for modern network environments.},
        keywords = {Intrusion Detection System (IDS), Machine Learning, Random Forest Algorithm, Cybersecurity, Network Security, KDD Cup 99 Dataset},
        month = {May},
        }