As we move further into a more digital supply chain, we have seen tremendous improvements in terms of efficiency, but we have also seen a rise in the risks that data vulnerabilities pose, particularly for intermediary supply chain nodes. As a result, traditional security measures have proven inadequate in protecting critical data that flows through these intermediary supply chain nodes, making them more susceptible to possible security breaches and tampering. In this paper, we introduce a novel solution that utilizes cryptography and Artificial Intelligence (AI)-powered predictive modeling for the security of supply chain data. Unlike other solutions that only protect endpoint data, our solution provides a more comprehensive security solution that extends cryptography for intermediary supply chain data, making it more secure and protected from possible breaches and tampering. To further improve our solution, we have utilized AI models, namely, XGBoost, Random Forest, and LightGBM, for predictive modeling. To prevent target leakage, features arithmetically derived from the recovery-duration target were excluded from the predictor set prior to training. The results indicate that, once the leakage-affected features are removed and hyperparameters are properly tuned, all five models achieve modest but genuine predictive accuracy, with R2 values in the range of 0.542–0.552. The best-performing model on this leakage-audited feature set is XGBoost, with an R2 value of 0.5525 and a test RMSE of 38.16 days, closely followed by Linear Regression and Ridge Regression (RMSE = 38.22 days, R2 = 0.5512), with the practical difference between the two being small (0.06 days RMSE) despite being statistically consistent across ten random splits. This demonstrates the solution’s effectiveness in reducing security risks while maintaining realistic, leakage-free predictive accuracy.
Alfaiz Madhiya, Vijay Solanki, K. Ahmed et al.· Computers· 0 citations
Due to the explosive growth in cloud-native infrastructures, the attack surface has dramatically increased in modern enterprise identity systems, where privilege escalation has become a major security risk. Conventional rule-based intrusion detection systems fall short in identifying multi-hop privilege inheritance paths and lateral movements over heterogeneous and dynamic identity graphs. This study introduces PEGraphSec-Net, a graph-theoretical framework for detecting privilege-escalation-relevant identity behavior, modeling cloud identity interactions as dynamic heterogeneous graphs of users, services, roles, tokens, and workloads. The core contribution of this framework is a graph-based detection pipeline—an Identity Relationship Graph Constructor, a Privilege-Escalation Path Encoder, and a Temporal Graph Attention Detection layer—evaluated on privilege-escalation-relevant attack categories using a documented proxy identity-graph construction derived from the UNSW-NB15 network-traffic benchmark, and benchmarked against six non-graph tabular classifiers (CNN, LightGBM, XGBoost, Random Forest, SVM, and MLP) trained under identical preprocessing; this pipeline achieves 98.78% accuracy, a weighted F1-score of 0.98692 (macro F1-score of 0.91828), and an AUC of 1.000 on the held-out test partition. PEGraphSec-Net is further benchmarked against three graph neural network baselines (GCN, GAT, and GraphSAGE) trained on the identical identity-graph topology and node attributes; all three substantially underperform PEGraphSec-Net (best case, GraphSAGE: 63.66% accuracy, 0.239 macro F1-score), indicating that a large share of PEGraphSec-Net’s performance derives from its explicit privilege-path encoding and temporal attention mechanisms rather than from the graph topology alone. An Adaptive Containment and Isolation Engine and a Mitigation Policy Reinforcement Optimizer are further proposed as risk-scoring and reward-driven policy-learning components, whose contribution is validated through module-wise ablation on classification performance; live containment action and reinforcement-learning-specific evaluation are left for future validation. The term “privilege escalation” is used throughout to denote the evaluated proxy attack categories (Exploits, Backdoor/Backdoors, and Reconnaissance) under a documented, decade-old (2015) network-intrusion benchmark, rather than production cloud-native IAM behavior, for which native-dataset validation remains an open direction. SHAP-based interpretability analysis links the model’s top-ranked traffic-level features back to the identity-graph risk, role, and trust-transition attributes they populate, evidencing that the learned representation captures semantically meaningful identity-behavior patterns within this proxy setting.
Md. Nuruzzaman Pranto, Md. Deluar Hossen, Mamunur R. Raja et al.· Computers· 0 citations
This paper presents a cloud-native, transformer-based multimodal intelligence framework that integrates Large Language Model semantic encoding with deep neural learning to enable automated defect prediction and proactive maintenance of large-scale digital infrastructure.
Mst Masuma Akter Semi, Md Masud Karim Rabbi, K. Ahmed et al.· Multimedia· 0 citations
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