Intrusion Detection and Prevention in Industrial IoT: A Technological Survey

Alruwaili, Fahad F.

doi:10.1109/iceccme52200.2021.9590961

Cited by 11 publications

(3 citation statements)

References 32 publications

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“…In [119], the authors surveyed the different types of attacks that an attacker can launch against IIoT equipment and mentioned ways to mitigate such attacks. In work [120], the authors reviewed intrusion-detection and -prevention mechanisms in the IIoT domain and highlight different protocols, algorithms, and mechanisms. In addition, they compared different mechanisms used to detect, prevent, and secure IIoT systems.…”

Section: Survey Discussionmentioning

confidence: 99%

Advanced Persistent Threats and Their Defense Methods in Industrial Internet of Things: A Survey

et al. 2023

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The industrial internet of things (IIoT) is a key pillar of the intelligent society, integrating traditional industry with modern information technology to improve production efficiency and quality. However, the IIoT also faces serious challenges from advanced persistent threats (APTs), a stealthy and persistent method of attack that can cause enormous losses and damages. In this paper, we give the definition and development of APTs. Furthermore, we examine the types of APT attacks that each layer of the four-layer IIoT reference architecture may face and review existing defense techniques. Next, we use several models to model and analyze APT activities in IIoT to identify their inherent characteristics and patterns. Finally, based on a thorough discussion of IIoT security issues, we propose some open research topics and directions.

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Section: Survey Discussionmentioning

confidence: 99%

Advanced Persistent Threats and Their Defense Methods in Industrial Internet of Things: A Survey

et al. 2023

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“…The security database stores the collected signatures of various attacks, and the data is preprocessed and sent to IDS for detection. IDS and Next Generation Firewall (NGFW) work together to maintain the security of IIoT [2]. Network traffic as a stream of data comes from one or more data sources [3], with high frequency and large data volume, and many data will not be saved.…”

Section: Introductionmentioning

confidence: 99%

Intrusion Detection Method Based on Active Incremental Learning in Industrial Internet of Things Environment

Sun¹,

Guo²,

Jin³

2022

Journal on Internet of Things

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Intrusion detection is a hot field in the direction of network security. Classical intrusion detection systems are usually based on supervised machine learning models. These offline-trained models usually have better performance in the initial stages of system construction. However, due to the diversity and rapid development of intrusion techniques, the trained models are often difficult to detect new attacks. In addition, very little noisy data in the training process often has a considerable impact on the performance of the intrusion detection system. This paper proposes an intrusion detection system based on active incremental learning with the adaptive capability to solve these problems. IDS consists of two modules, namely the improved incremental stacking ensemble learning detection method called Multi-Stacking model and the active learning query module. The stacking model can cope well with concept drift due to the diversity and generalization selection of its base classifiers, but the accuracy does not meet the requirements. The Multi-Stacking model improves the accuracy of the model by adding a voting layer on the basis of the original stacking. The active learning query module improves the detection of known attacks through the committee algorithm, and the improved KNN algorithm can better help detect unknown attacks. We have tested the latest industrial IoT dataset with satisfactory results.

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“…Introduction: Blockchain technology has drawn more attention in recent years due to its potential applications in various industries, including finance, healthcare, and cybersecurity [1]. In particular, developing Intrusion Detection System (IDS) based on blockchain technology could significantly improve the accuracy of intrusion detection while lowering false alarms and improving scalability [2]. As a result, this research aims to present a novel methodology in this paper that will improve the accuracy of blockchain-based IDS.…”

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confidence: 99%

An efficient blockchain‐based approach to improve the accuracy of intrusion detection systems

Abubakar,

Liu,

Gilliard

2023

Electronics Letters

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Intrusion Detection System (IDS) is a critical cybersecurity task that involves monitoring network traffic for malicious activity and taking appropriate action to stop it. However, insufficient training data or improperly chosen thresholds often limit the accuracy of such systems, resulting in high false‐positive rates. To improve the accuracy of an IDS, blockchain technology can be used as it provides a secure, decentralized, immutable ledger that can track suspicious activity over time and also identify intrusions globally. In this paper, the authors propose a novel methodology to improve the accuracy of blockchain‐based IDS. The approach combines different intrusion detection algorithms using a blockchain‐integrated architecture. It is based on the fusion principle and weighted votes, which the authors used to determine their results. The authors tested the system on DARPA 99 and MIT‐Lincoln Labs datasets using accuracy and false‐positive rate as their two metrics. The system achieved 92.6% accuracy and 7.4% false‐positive rates, indicating that the proposed system significantly increases the accuracy while reducing the false‐positive rate, opening up new opportunities for the development of highly accurate networks.

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Intrusion Detection and Prevention in Industrial IoT: A Technological Survey

Cited by 11 publications

References 32 publications

Advanced Persistent Threats and Their Defense Methods in Industrial Internet of Things: A Survey

Advanced Persistent Threats and Their Defense Methods in Industrial Internet of Things: A Survey

Intrusion Detection Method Based on Active Incremental Learning in Industrial Internet of Things Environment

An efficient blockchain‐based approach to improve the accuracy of intrusion detection systems

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