Paper Title
MACHINE LEARNING IN CYBERSECURITY: A MULTI-INDUSTRY CASE STUDY ANALYSIS FOR ENHANCED THREAT DETECTION AND RESPONSE

Abstract
Machine learning (ML) has rapidly emerged as a cornerstone technology in advancing cybersecurity across multiple industries. This review article provides a comprehensive analysis of how ML-driven techniques are being leveraged to strengthen threat detection, accelerate incident response, and improve the overall security posture in diverse operational landscapes, ranging from finance and healthcare to critical infrastructure and cloud-native environments. We begin by surveying the evolving threat landscape, highlighting the limitations of traditional signature-based approaches and the need for adaptive data-driven defences. Our synthesis of recent research and industrial deployments illustrates how state-of-the-art ML methods, including supervised, unsupervised, semi-supervised, and reinforcement learning, can effectively detect anomalies, identify zero-day vulnerabilities, classify malicious activities, and guide automated decision making. Drawing from real-world case studies, we examine the key factors influencing ML’s performance and reliability of ML, such as data quality, model interpretability, adversarial robustness, and integration into existing security architectures. We explore practical considerations for model selection, feature engineering, continuous learning, and lifecycle management to ensure both scalability and resilience. Additionally, we review novel approaches that combine ML with traditional cybersecurity tools and processes as well as the emerging role of federated learning and privacy-preserving techniques in safeguarding sensitive data. Through the integration of these findings, we offer a comprehensive strategic framework that enables researchers, practitioners, and policymakers to evaluate the current status of machine learning in the field of cybersecurity. Our analysis identified research gaps and future directions, including the potential of automated ML (AutoML), transfer learning, and causal inference to yield more adaptive, context-aware defences. Ultimately, this review offers a multi-industry perspective that underscores the transformative potential of ML in enhancing threat detection and response, guiding the field toward more robust and intelligent cybersecurity ecosystems.