An Explainable Deep Learning Approach for Distinguishing Cyber Attacks and Sensor Faults in Critical IoT Systems

References
- Moss, J., G. Wordon, Y. Duclos, Q. Liu, Q. Wang, J. Wang. Explainable AI in IoT: A Survey of Challenges, Advancements, and Pathways to Trustworthy Automation. – Electronics, Vol. 14, 2025, No 23, 4622.
- Suryateja, P. S., K. V. Rao. A Survey on Lightweight Cryptographic Algorithms in IoT. – Cybernetics and Information Technologies, Vol. 24, 2024, No 1, pp. 21-34.
- Sebestyen, H., D. E. Popescu, R. D. Zmaranda. A Literature Review on Security in the Internet of Things: Identifying and Analysing Critical Categories. – Computers, Vol. 14, 2025, No 2, 61.
- Elsayed, Z., A. Abdelgawad, N. Elsayed. Cybersecurity and Frequent Cyber Attacks on IoT Devices in Healthcare: Issues and Solutions. – arXiv Preprint, 2025.
- Tsimenidis, S., T. Lagkas, K. Rantos. Deep Learning in IoT Intrusion Detection. – Journal of Network and Systems Management, Vol. 30, 2022, No 1, 8.
- Sharma, S., K. Gupta, D. Gupta, S. Rani, G. Dhimn. An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces. – Computer Modeling in Engineering & Sciences, Vol. 138, 2024, No 3, 2029.
- Khraisat, A., A. Alazab. A Critical Review of Intrusion Detection Systems in the Internet of Things: Techniques, Deployment Strategy, Validation Strategy, Attacks, Public Datasets and Challenges. – Cybersecurity, Vol. 4, 2021, 18.
- Sarker, I. H., A. Kayes, S. Badsha, H. Alqahtani, P. Watters, A. Ng. Cybersecurity Data Science: An Overview from Machine Learning Perspective. – Journal of Big Data, Vol. 7, 2020, No 1, pp. 1-29.
- Imran, Y., Y. Xiang, L. Ali, A. S. Noor, K. Sarpong. CNN-GRU-FF: A Double-Layer Feature Fusion-Based Network Intrusion Detection System Using Convolutional Neural Network and Gated Recurrent Units. – Complex & Intelligent Systems, Vol. 10, 2024, pp. 1-18.
- Mallampati, S. B., H. Seetha. Enhancing Intrusion Detection with Explainable AI: A Transparent Approach to Network Security. – Cybernetics and Information Technologies, Vol. 24, 2024, No 1, pp. 98-117.
- Hassija, V., V. Chamola, A. Mahapatra, A. Singal, D. Goel, K. Huang, A. Hussain. Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence. – Cognitive Computation, Vol. 16, 2024, No 1, pp. 45-74.
- Ribeiro, M. T., S. Sing, C. Guestrin. Why Should I Trust You? Explaining the Predictions of Any Classifier. – In: Proc. ACM SIGKDD, 2016.
- Lundberg, S. M., S. I. Lee. A Unified Approach to Interpreting Model Predictions. – In: Advances in Neural Information Processing Systems, 2017, pp. 4765-4774.
- Vimbi, V., N. Shaffi, M. Mahmud. Interpreting Artificial Intelligence Models: A Systematic Review on the Application of LIME and SHAP in Alzheimer’s Disease Detection. – Brain Informatics, Vol. 11, 2024, No 1, 10.
- Sadhwani, S., M. A. H. Khan, R. Muthalagu, P. M. Pawar. A Hybrid BiLSTM-CNN Approach for Intrusion Detection for IoT Applications. – Scientific Reports, Vol. 16, 2025, No 1, 155.
- Zhang, C., J. Li, N. Wang, D. Zhang. Research on Intrusion Detection Method Based on Transformer and CNN-BiLSTM in Internet of Things. – Sensors, Vol. 25, 2025, No 9, 2725.
- Yaras, S., M. Dener. IoT-Based Intrusion Detection System Using New Hybrid Deep Learning Algorithm. – Electronics, Vol. 13, 2024, No 6, 1053.
- Cu i, B., Y. Chai, Z. Yang, K. Li. Intrusion Detection in IoT Using Deep Residual Networks with Attention Mechanisms. – Future Internet, Vol. 16, 2024, No 7, 255.
- Saiyed, M. F., I. Al-anbagi, M. S. Hossain. An Explainable Deep Learning System for Cyberattack Detection in Internet of Energy Networks. – IEEE Network, Vol. 40, 2026, No 2, pp. 39-46.
- Shtayat, M. M., M. K. Hasan, R. Sulaiman, S. Islam, A. U. R. Khan. An Explainable Ensemble Deep Learning Approach for Intrusion Detection in Industrial Internet of Things. – IEEE Access, Vol. 11, 2023, pp. 115047-115061.
- Bhowmik, A. TON_IoT Network Dataset. Kaggle, 2025.
- Sharma, K. P., T. Nagpal, T. Vora, A. Yadav, M. I. Abdullah, B. Jayaprakash, B. B. Bukate. Interpretable Intrusion Detection for IoT Environments Using a Self-Attention-Based Explainable AI Framework. – Scientific Reports, Vol. 15, 2025, No 1, 39937.
- Kopetz, H., W. Steiner. Internet of Things. Springer, 2022, pp. 325-341.
- Elhanashi, A., P. Dini, S. Saponara, Q. Zheng. Integration of Deep Learning into the IoT: A Survey of Techniques and Challenges for Real-World Applications. – Electronics, Vol. 12, 2023, No 24, 4925.
- Thakur, D., J. K. Saini, S. Srinivasan. DeepThink IoT: The Strength of Deep Learning in Internet of Things. – Artificial Intelligence Review, Vol. 56, 2023, No 12, pp. 14663-14730.
- Chinnasamy, R., M. Subramanian, S. V. Easwaramoorthy. Deep Learning-Driven Methods for Network-Based Intrusion Detection Systems: A Systematic Review. – ICT Express, Vol. 11, 2025, No 1, pp. 181-215.
- Li, B., J. Li, M. Jia. ADFCNN-BiLSTM: A Deep Neural Network Based on Attention and Deformable Convolution for Network Intrusion Detection. – Sensors, Vol. 25, 2025, No 5, 1382.
- Kowsher, M., A. Tahabilder, M. Z. I. Sanjid, N. J. Prottasha, M. S. Uddin, M. A. Hossain, M. A. K. Jilani. LSTM-ANN & BiLSTM-ANN: Hybrid Deep Learning Models for Enhanced Classification Accuracy. – Procedia Computer Science, Vol. 193, 2021, pp. 131-140.
- Natha, S., F. Ahmed, M. Siraj, M. Lagari, M. Altamimi, A. A. Chandio. Deep BiLSTM Attention Model for Spatial and Temporal Anomaly Detection in Video Surveillance. – Sensors, Vol. 25, 2025, No 1, 251.
- Hassouneh, N., S. Al-sharaeh. Intrusion Detection in IoT Networks Using LSTM Deep Learning Models with the UNSW-NB15 Dataset. – Proc. ICTCS, IEEE, 2025, pp. 263-269.
- Gupta, H., A. Jadhav, A. S. Bisht. Comparative Analysis of Machine and Deep Learning Models for Intrusion Detection in Fog-Enabled IoT Networks. – International Journal of Networked and Distributed Computing, Vol. 14, 2026, No 1.
- Abualhaj, M., H. Adeel, K. Masood, H. Soltani, H. Zemmouri, M. M. R. Aly, S. Mehmood. Comparative Analysis of LSTM-Based Variant Models for Detecting Attacks in IoT Networks. – Journal of Computing & Biomedical Informatics, Vol. 10, 2025, No 1.
- Tarek, R., A. Elshenawy, M. I. Assadwy, M. A. Madkour. Automated Diagnosis of Dental Diseases Using Deep Learning on Radiographic Images. – SN Computer Science, Vol. 6, 2025, No 6, 751.
- Sinha, J., M. Manollas. Efficient Deep CNN-BiLSTM Model for Network Intrusion Detection. – Proc. ICAIPR, ACM, 2020, pp. 223-231.
- Abduljabbar, R. L., H. Dia, P. W. Tsai. Development and Evaluation of Bidirectional LSTM Freeway Traffic Forecasting Models Using Simulation Data. – Scientific Reports, Vol. 11, 2021, No 1, 23899.
- Abrokwah-larbi, K. The Role of IoT and XAI Convergence in the Prediction, Explanation, and Decision of Customer Perceived Value (CPV) in SMEs: A Theoretical Framework and Research Proposition Perspective. – Discover Internet of Things, Vol. 5, 2025, No 1, 4.
- Dwivedi, R., D. Dave, H. Naik, S. Singhal, R. Omer, P. Patel. Explainable AI (XAI): Core Ideas, Techniques, and Solutions. – ACM Computing Surveys, Vol. 55, 2023, No 9.
- Wang, Y. A Comparative Analysis of Model-Agnostic Techniques for Explainable Artificial Intelligence. – Research Reports on Computer Science, 2024, pp. 25-33.
DOI: https://doi.org/10.2478/cait-2026-0027 | Journal eISSN: 1314-4081 | Journal ISSN: 1311-9702 (formerly 1314-4081)
Language: English
Page range: 70 - 94
Submitted on: Apr 1, 2026
Accepted on: Jun 12, 2026
Published on: Sep 9, 2026
Published by: Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
In partnership with: Paradigm Publishing Services
Keywords:
Related subjects:
© 2026 Nabeel I. Zanoon, Abdullah Odeh Al-Zaghameem, Khalid Alkharabsheh, published by Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.