Siren -- Advancing Cybersecurity through Deception and Adaptive Analysis

TL;DR

Siren is a cybersecurity system that uses deception, machine learning, and proactive threat analysis to actively engage and learn from cyber threats, enhancing defense capabilities.

Contribution

It introduces a novel integrated framework combining deception, adaptive machine learning, and simulated user interactions for proactive cybersecurity defense.

Findings

Dynamic link analysis with real-time classification

Enhanced threat engagement through honeypots with simulated activity

Improved threat detection and response capabilities

Abstract

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 realtime 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. Overall, 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.