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@article{172317,
        author = {Sandhya Gaikwad and Prof R.H.Ambole},
        title = {Review on Machine learning model for Efficient Botnet attack detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {3418-3424},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=172317},
        abstract = {The proliferation of the Internet of th¬ings (IoT) devices has resulted in a steady rise in the volume of IoT-based assaults. One of the most serious IoT risks is the IoT botnet attack, which tries to commit actual, effective, and profitable cybercrimes. IoT botnets are collections of Internet-connected IoT devices that have been infected with malware and are managed remotely by an attacker. ¬The Internet of th¬ings (IoT) systems have significant challenges in offering techniques to detect security vulnerabilities and assaults due to the rapid growth of threats and diversity in attack tactics. earlier identification would enable better IoT Botnet response proposals. As a result, it reduces the harm caused by possible assaults.},
        keywords = {Botnet, IoT, XGBoost, Decision tree, SVM, Accuracy, Precision, Recall.},
        month = {January},
        }