Research Area:  Internet of Things

Abstract:

Industrial Internet of Things (IIoT) applies Internet of Things (IoT) technology in industrial systems, to optimize business processes efficiency, service quality, and reliability. However, with a large of isolated IoT networks deployed in various industries, many vulnerabilities have been exposed to security incidents and posed threats to IIoT security. An intrusion detection system (IDS) is a security monitoring mechanism that promotes cyber security solutions for information systems. The system’s role is to detect abnormal activities of intruders and enable preventive measures to avoid risks. However, applying a traditional IDS-based solution to IIoT is challenging due to its particular characteristics such as resource-constrained, data privacy, and heterogeneity. Researchers are using the new emerging technologies such as Fog/Edge computing, Machine Learning (ML), Deep Learning (DL) to deploy an effective and adaptive IDS for various IIoT operating environments. This study focus is on the development of IDS in particular industrial environments. To this end, we provide a systemic review that addresses IDS deployment strategies, detection approaches, and methodologies and data sources used for evaluation. We also present some suggestions and challenges to be considered when designing IDS-based security for Industrial IoT as future research.

Keywords:  
Intrusion detection system (IDS)
Internet of thing (IoT)
Industry 4.0
Cyber security
Machine Learning (ML)
Deep Learning (DL)

Author(s) Name:  Djallel Hamouda; Mohamed Amine Ferrag; Nadjette Benhamida; Hamid Seridi

Publisher name:  IEEE

DOI:  10.1109/ICTAACS53298.2021.9715177

Volume Information:  

Paper Link:   https://ieeexplore.ieee.org/abstract/document/9715177