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

Abstract

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.

A Systematic Literature Review on Machine Learning and Deep Learning Approaches for Detecting DDoS Attacks in Software-Defined Networking

“…Another reason for resorting to a private dataset is the flexibility to evaluate their approaches in various scenarios. Finally, since there are no standards or criteria to set up the network environment and visualize the simulation scenarios, it is harder for researchers to compare their proposed approach with others [129]. Figure 15 shows two primary dataset types used by researchers.…”

“…Finally, some researchers contribute realistic synthetic datasets by publicly making them available[126,130,131]. The variety of datasets utilized results in the research community finding it challenging to compare their works with others[129]. Therefore, the availability of a realistic SDN dataset remains an open research challenge that needs addressing.•Distributed SDN controllers: This SLR remarked that the security approaches proposed in most literature studies are based on a topology with a single network controller, such as[69,75,77,86,113].…”

Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation

“…The network layer defines paths for the routing of data over the network. It has been demonstrated that low-power wireless networks with multiple hops are susceptible to a wide range of attacks, with routing attacks emerging as one of the most significant threats in the healthcare IoT environment [203]. There are many types of routing attacks, including selective forwarding attacks and replay attacks [204].…”

Fog-Assisted Deep-Learning-Empowered Intrusion Detection System for RPL-Based Resource-Constrained Smart Industries

“…The industrial sector is one of the highly privileged application areas of RPL, as the limited availability of resources and frequent communication breakages are crucial concerns in the industrial environment. RPL is a potential choice for coping with such emerging challenges in the industrial sector [ 14 , 15 , 16 ]. The expanding range of RPL applications in industrial sectors clearly shows its efficiency.…”