Mehak Khurana, PhD – Associate Professor, Department of Computer Engineering and Computational Sciences, School of Engineering, Applied Science, and Technology (SEAST), Canadian University Dubai, United Arab Emirates (UAE)
Shilpa Mahajan, PhD – Associate Professor, The NorthCap University, Haryana, India
Asif Ali Laghari, PhD – Assistant Professor, Sindh Madressatul Islam University, Karachi, Pakistan

Series: Computer Science, Technology and Applications; Cybercrime and Cybersecurity Research
BISAC: COM016000; COM053000; COM059000
DOI: https://doi.org/10.52305/QOKT6667

Unleashing Machine Learning (ML) for Advanced Threat Detection and Prevention 4.0 examines the transformative role of ML in enhancing cybersecurity. It explores how machine learning is revolutionizing cybersecurity in the era of smart industries. The book explores incorporating state-of-the-art ML approaches to detect, predict, prevent, and defend against complex cyberattacks in the age of IoT devices and critical infrastructures.

This book takes a deep dive into the challenges posed by cyber threats and presents innovative ML models that offer effective ways of identifying and mitigating these risks. The book delves into practical applications of various ML algorithms, including supervised, unsupervised, and hybrid models, which are employed to detect malicious activities in Cyber-Physical Production Systems (CPPSs). Readers will gain insights into data-driven security models, automated threat response mechanisms, and the latest advancements in AI-powered cybersecurity solutions. It outlines case studies that present the successful implementation of ML by bridging the gap between theory and practice in enhancing industrial security.

Unleashing Machine Learning (ML) for Advanced Threat Detection and Prevention 4.0 also addresses the difficulties faced in deploying ML in real-world scenarios, such as high false positive rates and the need for robust datasets, while proposing effective strategies to overcome these challenges. The book not only emphasizes the advantages of ML in threat detection but also addresses the potential risks associated with intensified connectivity and reliance on automated systems, urging a balanced approach to cybersecurity in the smart Industry.

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