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@article{195307,
        author = {Karedla Rupa Sri and Neelapu Vasanthi and Jagarapu Susmitha Laxmi and Gandi Venkata Satya Sai Varshith and K. Pavan Kumar},
        title = {Intrusion Detection System Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {123-125},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195307},
        abstract = {With the rapid growth of networked systems and internet-based services, cyber threats such as Distributed Denial of Service (DDoS), brute force attacks, and data exfiltration have become increasingly sophisticated. Traditional signature-based Intrusion Detection Systems (IDS) are ineffective against unknown and zero-day attacks. This paper presents a real-time Intrusion Detection System using Machine Learning trained on the CICIDS2017 dataset. The proposed system captures live network traffic using Scapy, extracts flow-based statistical features, and classifies traffic using a Random Forest classifier. The system provides real-time attack detection with confidence scores and supports offline training and evaluation. Experimental results demonstrate high detection accuracy and efficient real-time performance.},
        keywords = {Intrusion Detection System, Machine Learning, Random Forest, Network Security, CICIDS2017},
        month = {April},
        }