Abstract

Apps and private info now face smarter digital threats - like AI-powered hacks, unknown vulnerabilities, sudden IoT breaches, changing viruses - all because cloud tech grows fast. Old-style attack detectors don't work well anymore; they often cry wolf, break easily, or hide how they decide things. So here’s what we did: built a mix-model system using ConvD plus BiLSTM networks - that learns timing clues and layout traits in traffic - to spot odd behavior live. Instead of guessing why it flags something, we added SHapley analysis so every alert comes with plain reasons and ranked factors. This version catches more threats, shows its logic clearly to defenders, scales up smoothly, and fits real-world cloud use without hassle.

Keywords

  • Cloud security
  • Intrusion detection system
  • Conv1D
  • BiLSTM
  • Explainable AI
  • SHAP

References

  1. Shifa Shoukat, Tianhan Gao, Danish Javeed, Muhammad Shahid Saeed, Muhammad Adil, “Trust my IDS: An explainable AI integrated deep learning-based transparent threat detection system for industrial networks”, Computers & Security, Volume 149, Article Number 104191, 2025.
  2. Serrano, W. “CyberAIBot: Artificial Intelligence in an intrusion detection system for CyberSecurity in the IoT”. Future Generation Computer Systems, 166, 107543, 2025.
  3. Devendiran, R., & Turukmane, A. V.” Dugat-LSTM: Deep learning based network intrusion detection system using chaotic optimization strategy”. Expert Systems with Applications, 245, 123027, 2024.
  4. Vibhute, A. D., & Nakum, V.” Deep learning-based network anomaly detection and classification in an imbalanced cloud environment”. Procedia Computer Science, 232, 1636-1645, 2024.
  5. Alhayan, F., Alruwais, N., Alamgeer, M., Alashjaee, A. M., Abdullah, M., Khadidos, A. O., ... & Alshareef, A.” Design of advanced intrusion detection in cybersecurity using ensemble of deep learning models with an improved beluga whale optimization algorithm”. Alexandria Engineering Journal, 121, 90-102, 2025.
  6. Muhammad Sajid, Kaleem Razzaq Malik, Ahmad Almogren, Tauqeer Safdar Malik, Ali Haider Khan, Jawad Tanveer, Ateeq Ur Rehman, “Enhancing intursion detection: a hybrid machine and deep learning”, Journal of Cloud Computing: Advances, Systems and Applications, Volume 13, Article Number 123, 2024
  7. Prasanth, B. Karthikeyan, S. R. Gupta, “An Intrusion Detection System over IoT Data Streams Using Explainable Deep Learning”, Sensors, vol. 25, no. 3, pp. 847, 2025.
  8. Ahmed, J. Lee, M. Hassan, “Explainable Deep Learning Approach for Advanced Persistent Threats Detection”, Artificial Intelligence Review, vol. 37, no. 2, pp. 211-228, 2024.
  9. Kanumalli, S. S., Lavanya, K., Rajeswari, A., Samyuktha, P., & Tejaswi, M.” A scalable network intrusion detection system using bi-lstm and cnn”. In 2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS) (pp. 1-6). IEEE, 2023.
  10. Agomuo, O. C., Uzoma, A. K., Khan, Z., Otuomasirichi, A. I., & Muzamal, J. H.” Transparent AI for Adaptive Fraud Detection”. In 2025 19th International Conference on Ubiquitous Information Management and Communication (IMCOM) (pp. 1-6). IEEE, 2025.
  11. Nazreen Banu A and S.K.B. Sangeetha, "Intrumer: A Multi Module Distributed Explainable IDS/IPS for Securing Cloud Environment", Computers, Materials & Continua, Volume 82, Article Number 059805, 2025.
  12. Utsav Upadhyay, Alok Kumar, Umashankar Rawat, Satyabrata Roy, Sandeep Chaurasia, "Defending the Cloud: Understanding the role of Explainable AI in Intrusion Detection Systems", 2023 16th International Conference on Securityof Information and Networks (SIN), Article Number 10475080, 2023.
  13. Diogo Gaspar, Paulo Silva, and Catarina Silva, “Explainable AI for Intrusion Detection Systems: LIME and SHAP Applicability on Multi-Layer Perceptron”, IEEE Access, Volume 12, Article Number 3368377, 2024
  14. Jisna P, Jarin T, Praveen P N, “Advanced Intrusion Detection Using Deep Learning-LSTM Network On Cloud Environment”, 2021 Fourth International Conference on Microelectronics, Signals & Systems (ICMSS), Article Number 9673607, 2021
  15. Iqbal H. Sarker, Helge Janicke, Ahmad Mohsin, Asif Gill, Leandros Maglaras, “Explainable AI for cybersecurity automation, intelligence and trustworthiness in digital twin: Methods, taxonomy, challenges and prospects”, ICT Express, Volume 10, pp. 935-958, 2024.