Cybersecurity Threat Forecasting via Social Media Signals and Dark Web Monitoring: A Multi-Source Predictive Analytics Framework

Abstract

In the evolving landscape of cybersecurity, traditional reactive measures are increasingly inadequate against sophisticated threats. This research introduces a multi-source predictive analytics framework that leverages social media signals and dark web monitoring to forecast cybersecurity threats proactively. By integrating data from open-source platforms and clandestine forums, the framework employs machine learning algorithms to identify patterns indicative of impending cyber attacks. The study demonstrates the framework's efficacy in early threat detection, enabling organizations to implement preemptive security measures. This approach signifies a paradigm shift towards proactive cybersecurity, emphasizing the importance of diverse data sources in threat intelligence.