Anomaly classification in industrial Internet of things: A review

Rodríguez, Martha; Tobón, Diana P.; Munera, Danny

doi:10.1016/j.iswa.2023.200232

Cited by 5 publications

(1 citation statement)

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“…In the article, (Rodríguez et al, 2023) show the need to focus on learning methods, the quality of the data used and the importance of safety issues in free decisionmaking. This last point is crucial in terms of the cognitive dimension of the proposed solution.…”

Section: Literature Review Of Existing Modelsmentioning

confidence: 99%

Cybersecurity Mechanism for Automatic Detection of IoT Intrusions Using Machine Learning

Seyed,

Kebe,

El Arby

et al. 2024

Journal of Computer Science

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This article proposes an ML-based cyber security mechanism to optimize intrusion detection that attacks internet objects (IoT). Our approach consists of bringing together several learning methods namely supervised learning, unsupervised learning and reinforcement learning within the same Canvas. The objective is to choose among them the most optimal for classifying and predicting attacks while minimizing the impact linked to the learning costs of these attacks. In our proposed model, we have used a modular design to facilitate the implementation of the intrusion detection engine. The first Meta-learning module is used to collect metadata related to existing algorithmic parameters and learning methods in ML. As for the second module, it allows the use of a cost-sensitive learning technique so that the model is informed of the cost of intrusion detection scenarios. Therefore, among the ML classification algorithms, we choose the one whose automatic learning of intrusions is the least expensive in terms of its speed and its quality in predicting reality. This will make it possible to control the level of acceptable risk in relation to the typology of cyber-attacks. We then simulated our solution using the Weka tool. This led to questionable results, which can be subject to the evaluation of model performance. These results show that the classification quality rate is 93.66% and the classification consistency rate is 0.882 (close to unit 1). This proves the accuracy and performance of the model.

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Section: Literature Review Of Existing Modelsmentioning

confidence: 99%

Cybersecurity Mechanism for Automatic Detection of IoT Intrusions Using Machine Learning

Seyed,

Kebe,

El Arby

et al. 2024

Journal of Computer Science

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Variance‐driven security optimisation in industrial IoT sensors

Gupta,

Singh,

Kumar

et al. 2024

IET Networks

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The Industrial Internet of Things (IIoT) has transformed industrial operations with real‐time monitoring and control, enhancing efficiency and productivity. However, this connectivity brings significant security challenges. This study addresses these challenges by identifying abnormal sensor data patterns using machine learning‐based anomaly detection models. The proposed framework employs advanced algorithms to strengthen industrial defences against cyber threats and disruptions. Focusing on temperature anomalies, a critical yet often overlooked aspect of industrial security, this research fills a gap in the literature by evaluating machine learning models for this purpose. A novel variance‐based model for temperature anomaly detection is introduced, demonstrating high efficacy with accuracy scores of 0.92 and 0.82 on the NAB and AnoML‐IOT datasets, respectively. Additionally, the model achieved F1 scores of 0.96 and 0.89 on these datasets, underscoring its effectiveness in enhancing IIoT security and optimising cybersecurity for industrial processes. This research not only identifies security vulnerabilities but also presents concrete solutions to improve the security posture of IIoT systems.

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