Machine Learning Enabled Method for Preventing Industry 4.0 Botnet Attacks
Atul Kumar, Ishu Sharma
Abstract
The rapid expansion of Industry 4.0 technology has provided marvelous prospects for industrial growth, but it has also introduced new cybersecurity challenges, particularly the rise of botnet assaults. Machine learning technologies have emerged as useful solutions for enhancing the protection of Industry 4.0 platforms in response to these threats. This article investigates the use of machine learning approaches to prevent botnet assaults in Industry 4.0 environments. It evaluates the efficacy of the algorithm known as Random Forest in particular, which has proven exceptional capabilities in dealing with diverse and massive data. Random Forest achieves higher precision and recall scores by combining ensemble learning and robust generalizing, efficiently detecting and neutralizing botnet operations while minimizing false positives and negatives. The study’s findings underline the importance of using machine learning technologies, specifically Random Forest, to improve Industry 4.0 cybersecurity. These findings provide useful advice for future implementations, allowing enterprises to proactively protect their essential facilities and data against rising botnet threats.