IoT Security Using Machine Learning Methods




Hosseini Goki, Seyedamiryousef

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The rapid growth of internet-connected devices has made robust cybersecurity measures essential to protect against cyber threats. IoT cybersecurity includes various methods and technologies to secure internet-connected devices and systems from cyber attacks. The unique nature of IoT devices and systems poses several challenges to cybersecurity, including limited processing power, minimal security features, and vulnerability to attacks like DoS and DDoS. Cybersecurity strategies for IoT include encryption, authentication, access control, and threat detection and response, which utilize machine learning and artificial intelligence technologies to identify and respond to potential cyber attacks in real-time. The report discusses two projects related to cybersecurity in IoT environments, one focused on developing an intrusion detection system (IDS) based on deep learning algorithms to detect DDoS attacks, and another focused on identifying potential abnormalities in IoT networks using a fingerprint. These projects highlight the importance of prioritizing cybersecurity measures to protect against the growing number of cyber threats facing IoT devices and systems.



IoT, Security, Intrusion Detection System, Machine Learning, DDoS Attacks, Fingerprint