Hybrid X-AI Approach for Detecting Cyber Terrorism and Terrorism Threats Using BERT Transformer

Abstract

In an era marked by the rapid evolution of technology, cyber terrorism poses a significant threat to global security and societal stability. This paper proposes an X-AI enabled hybrid approach to enhance the detection and prevention of cyber terrorism activities. By integrating advanced artificial intelligence techniques with traditional cybersecurity measures, this approach aims to create a robust system capable of identifying and mitigating cyber threats in real-time. The proposed model leverages machine learning algorithms, including deep learning and ensemble methods, to analyze vast datasets for patterns indicative of cyber terrorist behavior. Additionally, the hybrid approach incorporates anomaly detection techniques to identify unusual activities that may signal an impending cyber-attack. Our system is designed to adapt continuously, learning from new data and evolving threat landscapes, thus ensuring proactive defence mechanisms against emerging cyber threats. We validate our approach through extensive experimentation on benchmark datasets, demonstrating improved accuracy and reduced false-positive rates compared to existing detection systems. The findings underscore the potential of X-AI technologies in fortifying cybersecurity infrastructures against cyber terrorism. This research not only contributes to the academic discourse on cybersecurity but also provides practical implications for organizations seeking to enhance their threat detection capabilities.