  Siren represents a pioneering research effort aimed at fortifying
cybersecurity through strategic integration of deception, machine learning, and
proactive threat 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. The research strives to
enhance user protection while yielding valuable insights for ongoing refinement
in response to the evolving landscape of cybersecurity threats.
