• Aditya Chauhan
• Er. Harpreet Kaur

Cyberattacks in the Internet of Things (IoT) are growing exponentially, especially zero-day attacks mostly driven by security weaknesses on IoT networks. Traditional intrusion detection systems (IDSs) adopted machine learning (ML), especially deep Learning (DL), to improve the detection of cyberattacks. DL-based IDSs require balanced datasets with large amounts of labelled data; however, there is a lack of such large collections in IoT networks. This paper proposes an efficient intrusion detection framework based on transfer learning (TL), knowledge transfer, and model refinement, for the effective detection of zero-day attacks. The framework is tailored to 5G IoT scenarios with unbalanced and scarce labelled datasets.

Attack Detection, IoT, Healthcare system, CNN etc.

"A REVIEW ON CNN BASED ATTACK DETECTION IN IOT HEALTHCARE SYSTEM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 6, page no.j622-j627, June-2023, Available :http://www.jetir.org/papers/JETIR2306972.pdf

"A REVIEW ON CNN BASED ATTACK DETECTION IN IOT HEALTHCARE SYSTEM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 6, page no. ppj622-j627, June-2023, Available at : http://www.jetir.org/papers/JETIR2306972.pdf