Challenges in detecting security threats in WoT: a systematic literature review

Challenges in detecting security threats in WoT: a systematic literature review

연구 분야: Infrastructure

학회: Artificial Intelligence Review

The rapid expansion of the Web of Things (WoT) and the Internet of Things (IoT) has raised security issues, with Denial of Service (DoS) attacks becoming increasingly prevalent. So, the aim of this study is to identify the security concerns in the four architectural layers of the Web of Things, particularly DoS attacks. For this study, existing literature are identified using search queries, and approximately 80 of relevant primary papers published in the recent decade are obtained after a thorough review which helps in addressing our research questions. After finding the relevant primary studies, we applied strict quality evaluation criteria to verify that all studies are evaluated. In addition, a taxonomy of deep learning (DL) techniques is presented on the basis of literature analysis conducted in this research, which is then used to characterize the various security concerns that occur in IoT and WoT systems. The study also examines which DL approaches are used to detect DoS/DDoS attacks in IoT and WoT. Our findings indicate that the optimal form of Intrusion Detection System (IDS) for dealing with DoS attacks is a hybrid IDS, which uses both the signature-based and the anomaly-based IDS. Moreover, DL techniques such as, CNNs and LSTMs, produced excellent results but are still in the development stage in terms of scalability and practical use. This review further highlights the present state of security mechanisms and sets the basis for future research, with an emphasis on refining DL-based techniques and improving the scalability and adaptability of security systems for WoT networks.