Explainable Artificial Intelligence for Cybersecurity Risk Assessment and Interpretable Detection of Emerging Network Attacks in Complex Information Systems
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V7I3P107Keywords:
Explainable Artificial Intelligence, Cybersecurity Risk Assessment, Network Attack Detection, Cyber Threats, Detection Systems, Information Systems Security, XAIAbstract
The growing complexity of complex interconnected information systems, cloud infrastructures and heterogeneous network environments has led to the need for new challenges in cyber security risk assessment and the detection of new network attacks. Traditional rule-based and machine learning security mechanisms are effective in detecting threats but can have trouble identifying new attack patterns, false-positive rates, and lack of transparency in decision making. This study tackles this limitedness by proposing an Explainable Artificial Intelligence (XAI) cybersecurity framework that combines cybersecurity risk assessment and interpretable network attack detection. The framework consists of machine learning based threat identification, risk scoring, anomaly detection, and explainability features to detect suspicious network traffic and give human-readable evidence on why a security decision has been made. The proposed framework is assessed with the representative cybersecurity network datasets and comparative experiments between traditional ML and XAI. Accuracy, precision, recall, F1-score, false-positive rate, latency of detection and measures related to explainability are used to evaluate predictive effectiveness and explainability. The experimental results show that the proposed approach can enhance the attack detection reliability and meaningful explanations for model predictions and cyber security risk factors. The study proposes an integrated and interpretable framework for cybersecurity risk assessment and emerging attack detection, which could assist security analysts with understanding the model decisions, prioritizing threats, and making more transparent decisions in complex information systems from the security perspective.
References
[1] Rjoub, G., Bentahar, J., Wahab, O. A., Mizouni, R., Song, A., Cohen, R., ... & Mourad, A. (2023). A survey on explainable artificial intelligence for cybersecurity. IEEE Transactions on Network and Service Management, 20(4), 5115-5140.
[2] Charmet, F., Tanuwidjaja, H. C., Ayoubi, S., Gimenez, P. F., Han, Y., Jmila, H., ... & Zhang, Z. (2022). Explainable artificial intelligence for cybersecurity: a literature survey. Annals of Telecommunications, 77(11), 789-812.
[3] Guo, Y. (2023). A review of machine learning-based zero-day attack detection: Challenges and future directions. Computer communications, 198, 175-185.
[4] Gaspar, D., Silva, P., & Silva, C. (2024). Explainable AI for intrusion detection systems: LIME and SHAP applicability on multi-layer perceptron. IEEE Access, 12, 30164-30175.
[5] Hariharan, K. B., Saravanan, S., & Parkunam, N. (2020). Life time improvement of D7 tool steel by cryogenic treatment. Materials Today: Proceedings, 21, 619-621.
[6] Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in neural information processing systems, 30.
[7] Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., ... & Lee, S. I. (2020). From local explanations to global understanding with explainable AI for trees. Nature machine intelligence, 2(1), 56-67.
[8] Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE access, 6, 52138-52160.
[9] Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., ... & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information fusion, 58, 82-115.
[10] Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2018). A survey of methods for explaining black box models. ACM computing surveys (CSUR), 51(5), 1-42.
[11] Alenezi, R., & Ludwig, S. A. (2021, December). Explainability of cybersecurity threats data using shap. In 2021 IEEE symposium series on computational intelligence (SSCI) (pp. 01-10). IEEE.
[12] Islam, M. T., Syfullah, M. K., Rashed, M. G., & Das, D. (2024). Bridging the gap: advancing the transparency and trustworthiness of network intrusion detection with explainable AI. International Journal of Machine Learning and Cybernetics, 15(11), 5337-5360.
[13] Wali, S., Farrukh, Y. A., & Khan, I. (2025). Explainable AI and random forest based reliable intrusion detection system. Computers & Security, 157, 104542.
[14] Khan, N., Ahmad, K., Al Tamimi, A., Alani, M. M., Bermak, A., & Khalil, I. (2025). Explainable AI-based intrusion detection systems for Industry 5.0 and adversarial XAI: A systematic review. Information, 16(12), 1036.
[15] Tabassi, E. (2023). Artificial intelligence risk management framework (AI RMF 1.0).
[16] Sharafaldin, I., Lashkari, A. H., & Ghorbani, A. A. (2018). Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1(2018), 108-116.
[17] Moustafa, N., & Slay, J. (2015, November). UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In 2015 military communications and information systems conference (MilCIS) (pp. 1-6). Ieee.
[18] Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. A. (2009, July). A detailed analysis of the KDD CUP 99 data set. In 2009 IEEE symposium on computational intelligence for security and defense applications (pp. 1-6). Ieee.
[19] Cutler, A., Cutler, D. R., & Stevens, J. R. (2012). Random forests. In Ensemble machine learning (pp. 157-175). Springer, New York, NY.
[20] Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).
[21] Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
[22] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.
[23] Minh, D., Wang, H. X., Li, Y. F., & Nguyen, T. N. (2022). Explainable artificial intelligence: a comprehensive review. Artificial Intelligence Review, 55(5), 3503-3568.
[24] Dhanushkodi, K., & Thejas, S. (2024). Ai enabled threat detection: Leveraging artificial intelligence for advanced security and cyber threat mitigation. IEEE access, 12, 173127-173136.
[25] Trillo, J. R., González-López, F., Morente-Molinera, J. A., Magán-Carrión, R., & García-Sánchez, P. (2025). Evaluation of explainable, interpretable and non-interpretable algorithms for cyber threat detection. Electronics, 14(15), 3073.
[26] Seknametla, P. R., & Sunkara, R. (2025). Applying AIOps for Predictive Incident Management in DevOps-Driven Cloud Infrastructure. International Journal, 12(6).
[27] Gantikota, S. (2026). Securing Microservice Communication across WCF, JAX-RS, and Spring Boot: Authentication, Authorization, and Audit Patterns for Healthcare Interoperability. American International Journal of Computer Science and Technology, 8(2), 15-20. https://doi.org/10.63282/3117-5481/AIJCST V8I2P102
[28] Sandra, K. (2024). THE REGULATED BANKING AI LAKEHOUSE. INDO-CONTINENTAL ACADEMIC PUBLISHERS.
[29] Brahmandam, L. M. K. (2025). Design Patterns and Empirical Evaluation of Reusable Terraform Modules Encoding Audit-Ready Defaults for Multi-Account AWS Deployments: A Cross-Team Study across EC2, S3, RDS, EKS, IAM, and Cloud Watch. International Journal of Emerging Research in Engineering and Technology, 6(2), 133-142. https://doi.org/10.63282/3050-922X.IJERET-V6I2P116
