A Text Similarity-based Protocol Parsing Scheme for Industrial Internet of Things

Jiang, Xiaoyu; Qiu, Tie; Zhou, Xiaobo; Zhang, Bin; Sun, Ximin; Chi, Jiancheng

doi:10.1109/cscwd49262.2021.9437659

Cited by 1 publication

(1 citation statement)

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“…The header section also extracts necessary features from packets using classification algorithms to design attack detection systems. In [ 159 , 160 ], the authors proposed models to captured packets from a trustworthy enterprise network and parsed them with different tools. In packet parsing-based NIDS design, the packets are grouped into protocol types.…”

Section: Nids Design Strategies For Iot Systemsmentioning

confidence: 99%

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

Gyamfi

Jurcut

2022

Sensors

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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.

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Section: Nids Design Strategies For Iot Systemsmentioning

confidence: 99%