Intrusion Detection in Internet of Things Systems: A Review on Design Approaches Leveraging Multi-Access Edge Computing, Machine Learning, and Datasets

Abstract

The explosive growth of the Internet of Things (IoT) applications has imposed a dramatic increase of network data and placed a high computation complexity across various connected devices. The IoT devices capture valuable information, which allows the industries or individual users to make critical live dependent decisions. Most of these IoT devices have resource constraints such as low CPU, limited memory, and low energy storage. Hence, these devices are vulnerable to cyber-attacks due to the lack of capacity to run existing general-purpose security software. It creates an inherent risk in IoT networks. The multi-access edge computing (MEC) platform has emerged to mitigate these constraints by relocating complex computing tasks from the IoT devices to the edge. Most of the existing related works are focusing on finding the optimized security solutions to protect the IoT devices. We believe distributed solutions leveraging MEC should draw more attention. This paper presents a comprehensive review of state-of-the-art network intrusion detection systems (NIDS) and security practices for IoT networks. We have analyzed the approaches based on MEC platforms and utilizing machine learning (ML) techniques. The paper also performs a comparative analysis on the public available datasets, evaluation metrics, and deployment strategies employed in the NIDS design. Finally, we propose an NIDS framework for IoT networks leveraging MEC.

Detection of Real-Time Malicious Intrusions and Attacks in IoT Empowered Cybersecurity Infrastructures

“…The computational cost was also analyzed in the study (running time) of each malware detection approach that employs extraction and machine learning technology. On the interconnected internet of things (IoT), [20], [21], [22], [23], [24], [25], [26] conducted a comparative analysis of intrusion detection techniques on the IoT. The study used the detection approach, IDS placement strategy, and security threat to classify IDSs for IoT [18].…”

Optimization algorithms in security and privacy-preserving data disturbance for collaborative edge computing social IoT deep learning architectures

“…Flexible and Advanced Internet of Things (FLEC) was proposed by author [14] as a way to combine traditional IoT with edge computing, which focuses on user positioning and environment adaptation. A genetic algorithm and a swarm optimization algorithm were presented in work [15] to maximize transfer time to solve load balancing issue in conventional data placement. MEC solution meets each and every one of the aforementioned requirements.…”

Deep Learning for Edge Computing Applications: A Comprehensive Survey

“…The IDS is used to forecast hacked devices and continuously monitor the services provided to LDs [8]. This is how the proposed cybersecurity framework operates.…”