چکیده مقاله
The unfettered growth that the Internet of Things IoT and the Internet of Medical Things IoMT has experienced has raised unprecedented opportunities for networking, automation, and decision making using the vast amounts of data gathered The increased adoption has also posed new advanced forms of security risks that range from the conventional types such as Distributed Denial of Service DDoS attack, Man in the Middle MitM attack, to the new forms that encompass Artificial Intelligence enabled systems, among others The conventional intruders detection systems appear to find it difficult to deal with the complexity posed by the growing use of IoMT devices This literature review offers an in depth and structured analysis of the role of the current state of AI enabled security measures for IoT and IoMT in improving threat detection, response times, and resilience It covers different models of AI, like classical Machine Learning ML , Deep Learning DL , Reinforcement Learning RL , Federated Learning FL , and Transfer Learning TL , while pointing out the pros and cons of each and analyzing which one to adapt to for different IoT applications Through the integration of findings presented in recent literature, this review not only groups existing approaches but also presents crucial trends, challenges, and future perspectives regarding AI based intrusion detection systems in both IoT and IoMT environments The information presented is expected to assist in empowering researchers and practitioners to devise resilient, adaptive, and efficient AI based security systems capable of meeting the dynamically changing threat environment in IoT networks
کلیدواژهها
نویسندگان
Negar Hosseini
۱ Department of Computer Engineering, Islamic Azad University West Tehran Branch Tehran, Iran
Abbas Mirzaei
۲ Department of Computer Engineering, Ard C , Islamic Azad University, Ardabil, Iran
بابک نوری مقدم
۳ Department of Computer Engineering, Germi C , Islamic Azad University, Germi, Iran
Sajjad Jahanbakhsh Gudakahriz
۴ Department of Computer Engineering, Germi C , Islamic Azad University, Germi, Iran
شیوه ارجاع
Hosseini, Negar and Mirzaei, Abbas and Nouri-Moghaddam, Babak and Jahanbakhsh Gudakahriz, Sajjad,1404,Artificial Intelligence for IoT and IoMT Security: A Comprehensive Review of Techniques, Challenges, and Applications,29th National Conference on Electrical, Computer and Mechanical Engineering,Shirvan
ارائهشده در
مجموعه مقالات بیست و نهمین کنفرانس ملی مهندسی برق ،کامپیوتر و مکانیک28 بهمن 1404 · شیروان