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@article{154937,
        author = {M. Ravi and V. Raja Shekhar and K. Ruthvik and T. Abhigna},
        title = {Machine Learning Techniques for Detecting Network Anomalies},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {12},
        pages = {1235-1238},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=154937},
        abstract = {As technology progresses, the number of internet users grows every day. Mobile technology has made communication more convenient, yet it is still insecure. Similarly, a number of internet-connected and data-sharing devices have been developed. These are Internet of Things (IoT) devices. The Internet of Things (IoT) is a rapidly growing industry with numerous uses. The number of people using IoT devices is expanding, as is the number of new goods. Users appreciate the functionality of IoT devices, but many are utterly oblivious of the security concerns that lurk beneath. As a result, improving data transfer and user privacy security is critical. Machine learning techniques are becoming more essential in identifying network breaches (or assaults), allowing network administrators to take preventative steps. To improve the performance of an Intrusion Detection System, we recommend using machine learning techniques.},
        keywords = { Intrusion Detection System, Machine Learning, security, Anomaly detection, Misuse detection, Classifiers, EDA.},
        month = {},
        }