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@article{170008,
        author = {Mohammed Akbar Zainool and C. Surekha and Abhishek Gupta and Mohammed Imran and T Shiva Kumar},
        title = {Detection of Cyber-Attacks in IoT using ML},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {3080-3084},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170008},
        abstract = {The rapid growth of Internet of Things (IoT) devices across various industries has led to the emergence of new security vulnerabilities, increasing the likelihood of cyber-attacks. As IoT technology becomes an integral part of everyday life, it is vital to implement strong security protocols to protect against threats that could have serious consequences. This research investigates how machine learning (ML) techniques can be applied to detect and mitigate security risks within IoT networks. We employ a combination of supervised, unsupervised, and hybrid ML models to identify key threats, including unauthorized access, data breaches, and denial-of-service (DoS) attacks. The study uses a detailed dataset based on transient network traffic in IoT systems for performance evaluation. Results indicate that ML methods outperform traditional security measures, offering greater accuracy in detecting malicious activities while reducing false positives and improving response times.},
        keywords = {IoT, Cybersecurity, Machine Learning, Anomaly Detection, Network Traffic Analysis, Unauthorized Access, Denial-of-Service (DoS).},
        month = {December},
        }