Siren: Advancing Cybersecurity through Deception and Adaptive Analysis

Drawing inspiration from mythical sirens, this project employs sophisticated methods to lure potential threats into controlled environments. The system features a dynamic machine learning model for real-time analysis and classification, ensuring continuous adaptability to emerging cyber threats.

The architectural framework includes a link monitoring proxy, a purpose-built machine learning model for dynamic link analysis, and a honeypot enriched with simulated user interactions to intensify threat engagement. Data protection within the honeypot is fortified with probabilistic encryption.

Additionally, the incorporation of simulated user activity extends the system’s capacity to capture and learn from potential attackers even after user disengagement. Siren introduces a paradigm shift in cybersecurity, transforming traditional defense mechanisms into proactive systems that actively engage and learn from potential adversaries.

This paper was presented at Computing Conference 2025, hosted in London, UK, on June 19–20, 2025 by the primary author of the paper.

Recommended citation: Samhruth, A., Girish, K., & Ganesh, N. (2025). "Siren: Advancing cybersecurity through deception and adaptive analysis." In Intelligent Computing: Proceedings of the Computing Conference, pp. 506–519. Springer LNNS. arXiv:2406.06225.
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