Machine Learning-Enabled Security Operations Centers
A New Paradigm for Real-Time Cyber Threat Mitigation
Keywords:
Machine Learning, Security Operations Center, Cybersecurity, Threat Detection, Real-Time Mitigation, Artificial IntelligenceAbstract
The increasing complexity of cyber threats necessitates the evolution of Security Operations Centers (SOCs) to enhance their efficiency and effectiveness in real-time threat mitigation. This paper explores the integration of machine learning (ML) models into SOCs, emphasizing their potential to revolutionize cybersecurity practices. It discusses various ML techniques, such as supervised and unsupervised learning, and their applications in threat detection and response. Moreover, the paper examines the benefits of implementing ML in SOCs, including improved accuracy, reduced false positives, and faster response times. Furthermore, it highlights the challenges faced in adopting these technologies and provides recommendations for organizations seeking to enhance their cybersecurity posture through ML-enabled SOCs. The findings suggest that the integration of ML into SOCs represents a significant advancement in proactive threat management, enabling organizations to respond more effectively to an ever-evolving threat landscape.
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