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@article{170365,
        author = {Sai Venkat and K.Rahul and T.Rahul and Ch.Lohith},
        title = {Intrusion Detection System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {363-366},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170365},
        abstract = {Intrusion Detection Systems are considered key elements in maintaining network security that scrutinize traffic to identify possible threats. This work envisions the construction of a machine-learning based IDS. The NSL-KDD dataset, one of the most common benchmarks for network intrusion detection, is used. Two algorithms: Isolation Forest for anomaly detection, and Random Forest for attack classification, were implemented along with a thorough evaluation of the metrics of accuracy, precision, and recall. Results The findings reveal the potential ability of machine learning approaches to improve the detection accuracy of intrusions with minimal false positives. The system, then, holds significant promise for real-time application within cybersecurity infrastructures.},
        keywords = {Intrusion Detection System, Machine Learning, Anomaly Detection, NSL-KDD, Cybersecurity, Random Forest, Isolation Forest},
        month = {December},
        }