Detecting Anomalous Network Behavior Through Real Time Flow Monitoring and Threat Intelligence
Keywords:
Network Anomaly Detection, Flow Monitoring, Machine Learning, Threat Intelligence, Cybersecurity, Real-Time AnalysisAbstract
The rapid expansion of networked systems and the sophistication of cyber threats have highlighted the limitations of traditional, signature-based security measures. This study explores the convergence of real-time flow monitoring with dynamic threat intelligence to identify anomalous network behavior effectively. By leveraging machine-learning models and enriched threat feeds, organizations can detect zero-day attacks, lateral movements, and stealthy intrusions with greater accuracy. The paper proposes a layered approach where network telemetry is fused with contextual threat data to enable proactive detection mechanisms. This model serves as a foundation for resilient security architectures in dynamic enterprise environments.
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Copyright (c) 2026 Sergio Navarro (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
