Data Protection Methods and Intrusion Detection Systems Employing Machine Learning and Deep Learning in the IoT: A Comparative Review
Zahraa Majeed Al- Khuzaie, Salah Abdulhadi Albermany, Mohammed Ahmed AbdlNibe · 2022 Second International Conference on Computer Science, Engineering and Applications (ICCSEA) · 2022
Scarcely any Intrusion detection systems (IDS) are based on the principle of Machine Learning (ML). Such systems are the potential next-generation tool for enhancing cyber security in networks. These systems can detect zero-day attacks. These attacks are anonymous and unorthodox for researchers and users. Recently, various advanced attacks have been occurring simultaneously with the unprecedented development of the Internet. To detect such attacks, both Machine Learning (ML) and Deep Learning (DL) have been introduced. Therefore, researchers have proposed several methods for detecting any advanced attacks. In this paper, a thorough classification is introduced for algorithms of Machine Learning (ML) and Deep Learning (DL). In addition, the detailed survey will cast light on various methods adopted in detection attacks. All of these methods are based on the principles of DL and ML. Further, many platforms and devices will be introduced both of which are used in executing the methods of the DL and ML and in suggesting security solutions that could be utilized within the field of the Internet of Things (IoT).