Machine and deep learning techniques for detecting internet protocol version six attacks: a review

The rapid development of information and communication technologies has increased the demand for internet-facing devices that require publicly accessible internet protocol (IP) addresses, resulting in the depletion of internet protocol version 4 (IPv4) address space. As a result, internet protocol version 6 (IPv6) was designed to address this issue. However, IPv6 is still not widely used because of security concerns. An intrusion detection system (IDS) is one example of a security mechanism used to secure networks. Lately, the use of machine learning (ML) or deep learning (DL) detection models in IDSs is gaining popularity due to their ability to detect threats on IPv6 networks accurately. However, there is an apparent lack of studies that review ML and DL in IDS. Even the existing reviews of ML and DL fail to compare those techniques. Thus, this paper comprehensively elucidates ML and DL techniques and IPv6-based distributed denial of service (DDoS) attacks. Additionally, this paper includes a qualitative comparison with other related works. Moreover, this work also thoroughly reviews the existing ML and DL-based IDSs for detecting IPv6 and IPv4 attacks. Lastly, researchers could use this review as a guide in the future to improve their work on DL and ML-based IDS

A Systematic Literature Review on Machine and Deep Learning Approaches for Detecting Attacks in RPL-Based 6LoWPAN of Internet of Things

The IETF Routing Over Low power and Lossy network (ROLL) working group defined IPv6 Routing Protocol for Low Power and Lossy Network (RPL) to facilitate efficient routing in IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN). Limited resources of 6LoWPAN nodes make it challenging to secure the environment, leaving it vulnerable to threats and security attacks. Machine Learning (ML) and Deep Learning (DL) approaches have shown promise as effective and efficient mechanisms for detecting anomalous behaviors in RPL-based 6LoWPAN. Therefore, this paper systematically reviews and critically analyzes the research landscape on ML, DL, and combined ML-DL approaches applied to detect attacks in RPL networks. In addition, this study examined existing datasets designed explicitly for the RPL network. This work collects relevant studies from five major databases: Google Scholar, Springer Link, Scopus, Science Direct, and IEEE Xplore® digital library. Furthermore, 15,543 studies, retrieved from January 2016 to mid-2021, were refined according to the assigned inclusion criteria and designed research questions resulting in 49 studies. Finally, a conclusive discussion highlights the issues and challenges in the existing studies and proposes several future research directions