Threat Hunting in Cloud Infrastructure Using Behavioral Analytics and Machine Learning
Abstract
Proactive threat hunting is essential for identifying advanced persistent threats (APTs) in cloud infrastructure. This paper presents a threat-hunting framework that combines behavioral analytics with machine learning to detect and investigate suspicious activity. The framework analyzes user behavior, API calls, and network traffic to identify deviations from baseline patterns. Machine learning models are used to classify anomalies and generate actionable threat intelligence. Performance analysis shows that the framework reduces false positives and improves threat detection accuracy. The study concludes that behavioral-based threat hunting enhances the security posture of cloud environments by enabling early detection of sophisticated attacks.
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