Repository logo
  • English
  • Català
  • Čeština
  • Deutsch
  • Español
  • Français
  • Gàidhlig
  • Italiano
  • Latviešu
  • Magyar
  • Nederlands
  • Polski
  • Português
  • Português do Brasil
  • Suomi
  • Svenska
  • Türkçe
  • Tiếng Việt
  • Қазақ
  • বাংলা
  • हिंदी
  • Ελληνικά
  • Yкраї́нська
  • Log In
    New user? Click here to register.Have you forgotten your password?
Repository logo
  • Communities & Collections
  • All of DSpace
  • English
  • Català
  • Čeština
  • Deutsch
  • Español
  • Français
  • Gàidhlig
  • Italiano
  • Latviešu
  • Magyar
  • Nederlands
  • Polski
  • Português
  • Português do Brasil
  • Suomi
  • Svenska
  • Türkçe
  • Tiếng Việt
  • Қазақ
  • বাংলা
  • हिंदी
  • Ελληνικά
  • Yкраї́нська
  • Log In
    New user? Click here to register.Have you forgotten your password?
  1. Home
  2. Browse by Author

Browsing by Author "Rayampalli Prasen Akhil Babu 1NH22EC131"

Now showing 1 - 1 of 1
Results Per Page
Sort Options
  • Loading...
    Thumbnail Image
    Item
    X-AI Enabled Hybrid Approach for Detection of Cyber Terrorism
    (2026-02-11) Adoni Mahammad Rehan 1NH22CS011; Avula Reddy Bharath Yadav 1NH22CS034; Rayampalli Prasen Akhil Babu 1NH22EC131; Pittu Mahendranath Reddy 1NH22EC114
    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.

DSpace software copyright © 2002-2026 LYRASIS

  • Cookie settings
  • Privacy policy
  • End User Agreement
  • Send Feedback