A Comparative Study of Lightweight Machine Learning Techniques for Cyber-Attacks Detection in Blockchain-Enabled Industrial Supply Chain

【Author】 Ismail, Shereen; Dandan, Salah; Dawoud, Diana W.; Reza, Hassan

【Source】IEEE ACCESS

【影响因子】3.476

【Abstract】The security of Industrial Supply Chain (ISC) has emerged through the integration of Industrial Internet of Things (IIoT) and Blockchain (BC) technology. This new era involves effectively protecting IIoT systems from various threats and ensuring their smooth operation and resilience against potential cyber-attacks. Within the ISC ecosystem, combining machine learning (ML)-based security models for cyber-attack detection can play a crucial role in enhancing the ISC security and proactively identifying potential threats. This paper presents a BC-enabled ISC that embed ML security model integrated within a multi-layered approach. We conducted a comparative study and performance analysis of several ML classification techniques, with a focus on supervised methods to identify the lightweight model for cyber-attack detection suitable for deployment in resource-constrained IIoT environment. We investigate the performance of Gaussian Naive Bayes (NB), K-Nearest Neighbors (KNN), Random Forest (RF), Decision Tree (DT), and three ensemble techniques, namely Bagging, Stacking, and Boosting. The study employs the WUSTL-IIOT-2021 imbalanced dataset, which contains samples representing four types of attacks, including denial of service (DoS), SQL injection, reconnaissance, and backdoor. The paper addresses the imbalance in class representation by customizing the dataset for training and testing the ML models. Both Mutual Information (MI) and Extra-trees (ET) are applied as a one-stage ensemble feature selection. The performance of the ML models are investigated using classification accuracy (Acc), precision, recall, F1 score, Matthews correlation coefficient (MCC), model size (Mem), training time (TT) and prediction time (PT).

【Keywords】Industrial Internet of Things; Security; Cyberattack; Biological system modeling; Nearest neighbor methods; Radio frequency; Monitoring; Supply chains; Detection algorithms; Blockchains; Industrial supply chain; industrial Internet of Things; security; cyber-attacks; detection; machine learning; blockchain; lightweight

【发表时间】2024

【收录时间】2024-08-14

【文献类型】实验仿真

【主题类别】

区块链应用-实体经济-物联网