Recent Papers

As Industry 4.0 transforms manufacturing with interconnected CPPS, a growing need to integrate more advanced machine-learning techniques into existing cybersecurity frameworks has been realized. In this respect, the growing reliance upon real-time data transfer and networked devices of industrial environments intrinsically presents severe vulnerabilities to cyber-attacks that traditional security measures are not designed too effectively counter. The paper focuses on the exploitation of different machine learning algorithms such as Decision Trees, Random Forests, SVMs, and Naive Bayes in detection and prevention against cyber threats within Industry 4.0 ecosystems. This detection gets enhanced with sophisticated attack detection capabilities through traffic pattern analysis and anomaly detection. Experimental tests within an industrial network prove these machine learning models make threat detection much more accurate and faster, hence drastically reducing unauthorized access and unwanted disturbance. The results tell clearly of the potential of machine learning to secure not just better security measures but also enable secure and scalable adoption of Industry 4.0 technologies.

Copyright © 2024 T.Chandrasekhar. This is an open access article distributed under the Creative Commons Attribution License.