A Linear Probabilistic Resilience Model for Securing Critical Infrastructure in Industry 5.0

Abuhasel, Khaled Ali

doi:10.1109/access.2023.3300650

Cited by 5 publications

(1 citation statement)

References 32 publications

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“…Their findings show that their approach achieves an accuracy of 96.51% compared with other baseline works. Alternatively, in [28,37,38], the authors are achieving noteworthy accuracy in bifurcating malicious and non-malicious critical infrastructure data. The proposed work employs XAI techniques for efficient feature selection and anomaly detection to remove anomalies from the utilized dataset.…”

Section: Performance Analysis Of Ai Classifiermentioning

confidence: 99%

AI and Blockchain-Based Secure Data Dissemination Architecture for IoT-Enabled Critical Infrastructure

Rathod,

Jadav,

Tanwar

et al. 2023

Sensors

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The Internet of Things (IoT) is the most abundant technology in the fields of manufacturing, automation, transportation, robotics, and agriculture, utilizing the IoT’s sensors-sensing capability. It plays a vital role in digital transformation and smart revolutions in critical infrastructure environments. However, handling heterogeneous data from different IoT devices is challenging from the perspective of security and privacy issues. The attacker targets the sensor communication between two IoT devices to jeopardize the regular operations of IoT-based critical infrastructure. In this paper, we propose an artificial intelligence (AI) and blockchain-driven secure data dissemination architecture to deal with critical infrastructure security and privacy issues. First, we reduced dimensionality using principal component analysis (PCA) and explainable AI (XAI) approaches. Furthermore, we applied different AI classifiers such as random forest (RF), decision tree (DT), support vector machine (SVM), perceptron, and Gaussian Naive Bayes (GaussianNB) that classify the data, i.e., malicious or non-malicious. Furthermore, we employ an interplanetary file system (IPFS)-driven blockchain network that offers security to the non-malicious data. In addition, to strengthen the security of AI classifiers, we analyze data poisoning attacks on the dataset that manipulate sensitive data and mislead the classifier, resulting in inaccurate results from the classifiers. To overcome this issue, we provide an anomaly detection approach that identifies malicious instances and removes the poisoned data from the dataset. The proposed architecture is evaluated using performance evaluation metrics such as accuracy, precision, recall, F1 score, and receiver operating characteristic curve (ROC curve). The findings show that the RF classifier transcends other AI classifiers in terms of accuracy, i.e., 98.46%.

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Section: Performance Analysis Of Ai Classifiermentioning

confidence: 99%

AI and Blockchain-Based Secure Data Dissemination Architecture for IoT-Enabled Critical Infrastructure

Rathod,

Jadav,

Tanwar

et al. 2023

Sensors

View full text Add to dashboard Cite

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The research landscape of industry 5.0: a scientific mapping based on bibliometric and topic modeling techniques

Rejeb,

Zrelli

et al. 2024

Flex Serv Manuf J

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Industry 5.0 (I5.0) marks a transformative shift toward integrating advanced technologies with human-centric design to foster innovation, resilient manufacturing, and sustainability. This study aims to examine the evolution and collaborative dynamics of I5.0 research through a bibliometric analysis of 942 journal articles from the Scopus database. Our findings reveal a significant increase in I5.0 research, particularly post-2020, yet highlight fragmented collaboration networks and a noticeable gap between institutions in developed and developing countries. Key thematic areas identified include human-robot collaboration, data management and security, AI-driven innovation, and sustainable practices. These insights suggest that a more integrated approach is essential for advancing I5.0, calling for strengthened global collaborations and a balanced emphasis on both technological and human-centric elements to fully realize its potential in driving resilient and sustainable industrial practices. This study provides the first comprehensive bibliometric analysis of I5.0, offering valuable insights for both researchers and practitioners.

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AI Enabled Threat Detection: Leveraging Artificial Intelligence for Advanced Security and Cyber Threat Mitigation

Dhanushkodi,

Thejas

2024

IEEE Access

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A Linear Probabilistic Resilience Model for Securing Critical Infrastructure in Industry 5.0

Cited by 5 publications

References 32 publications

AI and Blockchain-Based Secure Data Dissemination Architecture for IoT-Enabled Critical Infrastructure

AI and Blockchain-Based Secure Data Dissemination Architecture for IoT-Enabled Critical Infrastructure

The research landscape of industry 5.0: a scientific mapping based on bibliometric and topic modeling techniques

AI Enabled Threat Detection: Leveraging Artificial Intelligence for Advanced Security and Cyber Threat Mitigation

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