Enhancing Threat Detection in Enterprise Environments Through Behavioral Anomaly Analysis and Machine Learning Techniques

Tomoka Shibasaki Yoko

Authors

Tomoka Shibasaki Yoko Cybersecurity Data Scientist, Author

Keywords:

Cybersecurity, Machine Learning, Behavioral Anomaly Detection, Threat Intelligence, Enterprise Security, Supervised Learning, Intrusion Detection Systems, Data Analytics

Abstract

Behavioral anomaly detection has emerged as a significant approach in cybersecurity, enabling systems to identify and respond to threats beyond traditional rule-based methods. This paper explores how machine learning techniques, especially those involving behavioral analytics, can enhance enterprise-level threat detection capabilities. Through a combination of supervised and unsupervised learning models, organizations can proactively identify deviations from normal activity that signal potential intrusions or malicious behavior. The study further evaluates the integration of these models into enterprise infrastructure, highlighting performance metrics and cost considerations

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