An Explainable Machine Learning Framework for Predictive Cybersecurity in Computational Systems
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V2I1P102Keywords:
Predictive Cybersecurity, Explainable Ai (Xai), Shap, Counterfactual Explanations, Graph-Based Detection, Time-Series Anomaly Detection, Adversarial Robustness, Mlops, Data Drift Monitoring, Soc Automation, Risk Scoring, Privacy-Preserving AnalyticsAbstract
This paper proposes an explainable machine learning (XML) framework for predictive cybersecurity in computational systems spanning cloud, edge, and on-premise environments. The framework unifies three layers: (1) a data and feature layer that fuses multivariate time-series telemetry (network flows, host logs, API traces) with graph-structured context (asset and identity relationships) and privacy-preserving enrichment; (2) a modeling layer combining calibrated anomaly detection and supervised risk scoring, where temporal models capture bursty behaviors and graph models detect lateral movement patterns; and (3) an explainability and operations layer that delivers human-interpretable justifications, policy-ready signals, and feedback loops for continuous improvement. Explanations are generated at both local and global levels using SHAP- and counterfactual-based analyses, rule induction, and causal attributions to highlight high-leverage indicators (e.g., rare process chains, privilege escalation motifs). The framework supports drift monitoring, adversarial robustness checks, and cost-aware thresholding to minimize alert fatigue. It integrates with SOC workflows via MLOps pipelines, providing lineage, versioning, and pre-deployment evaluation. In experimental validation on heterogeneous security datasets and synthetic red-team scenarios, the framework improves early-warning lead time and detection quality while preserving operator trust through concise, actionable rationales that map directly to containment playbooks. We discuss governance and compliance considerations, including auditability and data minimization, and provide reference templates for deployment in regulated industries. The result is a pragmatic path to measurable, explainable, and continuously learnable cyber defense
References
[1] Lundberg, S. M., & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. NeurIPS. https://arxiv.org/abs/1705.07874
[2] Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why Should I Trust You?”: Explaining the Predictions of Any Classifier. KDD. https://arxiv.org/abs/1602.04938
[3] Sundararajan, M., Taly, A., & Yan, Q. (2017). Axiomatic Attribution for Deep Networks. ICML. https://arxiv.org/abs/1703.01365
[4] Ying, Z., Bourgeois, D., You, J., Zitnik, M., & Leskovec, J. (2019). GNNExplainer: Generating Explanations for Graph Neural Networks. NeurIPS. https://arxiv.org/abs/1903.03894
[5] Wachter, S., Mittelstadt, B., & Russell, C. (2017). Counterfactual Explanations without Opening the Black Box. Harvard Journal of Law & Technology. https://arxiv.org/abs/1711.00399
[6] Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., & Elhadad, N. (2015). Intelligible Models for Healthcare: Predicting Pneumonia Risk and Hospital 30-day Readmission. KDD. https://arxiv.org/abs/1511.01644
[7] Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017). On Calibration of Modern Neural Networks. ICML. https://arxiv.org/abs/1706.04599
[8] Bai, S., Kolter, J. Z., & Koltun, V. (2018). An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. arXiv. https://arxiv.org/abs/1803.01271
[9] Vaswani, A., et al. (2017). Attention Is All You Need. NeurIPS. https://arxiv.org/abs/1706.03762
[10] Kipf, T. N., & Welling, M. (2016). Semi-Supervised Classification with Graph Convolutional Networks. ICLR. https://arxiv.org/abs/1609.02907
[11] Moustafa, N., & Slay, J. (2015). UNSW-NB15: A Comprehensive Data Set for Network Intrusion Detection Systems. MILCOM. https://arxiv.org/abs/1511.06770
[12] Sharafaldin, M., Lashkari, A. H., & Ghorbani, A. A. (2018). Toward Generating a New Intrusion Detection Dataset (CIC-IDS2017). ICISSP. https://www.unb.ca/cic/datasets/ids-2017.html
[13] Kent, A. D. (2015). Cybersecurity Data Sources for Dynamic Network Research: The LANL Authentication Dataset. Los Alamos National Laboratory. https://csr.lanl.gov/data/
[14] Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation Forest. ICDM. https://doi.org/10.1109/ICDM.2008.17
[15] Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the Support of a High-Dimensional Distribution. Neural Computation. https://direct.mit.edu/neco/article/13/7/1443/6892
[16] Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. KDD. https://arxiv.org/abs/1603.02754
[17] Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). CatBoost: Unbiased Boosting with Categorical Features. NeurIPS. https://arxiv.org/abs/1706.09516
[18] Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. (2014). A Survey on Concept Drift Adaptation. ACM Computing Surveys. https://doi.org/10.1145/2523813
[19] Sculley, D., et al. (2015). Hidden Technical Debt in Machine Learning Systems. NeurIPS. https://arxiv.org/abs/1701.04112
[20] Goodfellow, I. J., Shlens, J., & Szegedy, C. (2015). Explaining and Harnessing Adversarial Examples. ICLR. https://arxiv.org/abs/1412.6572
[21] Ribeiro, M. T., Singh, S., & Guestrin, C. (2018). Anchors: High-Precision Model-Agnostic Explanations. AAAI. https://arxiv.org/abs/1802.07623
[22] Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. ICCV. https://arxiv.org/abs/1610.02391
