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@article{197052,
        author = {Sujeetha R and Mrs.S.Sasikala and Priya A and Sindhuja J},
        title = {Sujeetha R},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {5544-5549},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197052},
        abstract = {With the rapid growth of internet usage and digital communication, network security has become an essential concern for organizations and individuals. Cyber threats such as unauthorized access, denial-of- service attacks, and malicious activities can compromise sensitive data and disrupt network operations. Traditional security mechanisms are often unable to detect complex and evolving attacks effectively. This research proposes a Network Intrusion Detection System (NIDS) based on Machine Learning techniques to identify abnormal network behavior. The model utilizes the Random Forest algorithm to classify network traffic as either normal or malicious. The system is trained and evaluated using the NSL-KDD dataset, which contains labeled records representing both normal activities and different attack categories. The experimental results demonstrate that the proposed approach improves detection accuracy and enhances the reliability of intrusion detection systems. This work highlights the potential of machine learning methods in strengthening modern network security solutions},
        keywords = {Machine Learning, Network Intrusion Detection System (IDS), Network Security, Random Forest Algorithm, NSL- KDD Dataset, Real-Time Traffic Monitoring, Scapy, Flask Web Application.},
        month = {April},
        }