X-AI Enabled Hybrid Approach for Detection of Cyber Terrorism
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Date
2026-02-11
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Abstract
Cyber terrorism has become a significant threat in today’s connected world. Attackers use social media, networks, and online platforms to spread extremist content and carry out harmful activities. Traditional cybersecurity methods often struggle to keep up with the fast-changing behaviors and tactics of cyber terrorists. This project introduces a hybrid approach powered by X-AI that combines artificial intelligence with clear decision making to improve the detection and prevention of cyber terrorism. The proposed system uses several machine learning and deep learning algorithms, including BERT, LSTM, GRU, Naïve Bayes, and Random Forest. BERT captures the context of tweets and text data, while LSTM and GRU analyze sequence patterns. Ensemble models like Random Forest provide strong classification. Explainable AI tools such as SHAP and LIME offer transparency in predictions, helping analysts understand why a tweet or behavior is flagged as harmful.