arXiv subjects

Fatemeh Moradi

Publications and source records attributed to Fatemeh Moradi.

2 recordsLinked to original sources

arXiv2026

Information-Theoretic Digital Twins for Stealthy Attack Detection in Industrial Control Systems: A Closed-Form KL Divergence Approach

Digital twins (DTs) are increasingly used to monitor and secure Industrial Control Systems (ICS), yet detecting stealthy False Data Injection Attacks (FDIAs) that manipulate system states within normal physical bounds remains challenging. Deep learning anomaly detectors often over-generalize such subtle manipulations, while classical fault detection methods do not scale well in highly correlated multivariate systems. We propose a closed-loop Information-Theoretic Digital Twin (IT-DT) framework for real-time anomaly detection. N4SID identification is combined with steady-state Kalman filtering to quantify residual distribution shifts via closed-form KL divergence, capturing both mean deviations and malicious cross-covariance shifts. Evaluations on the SWaT and WADI datasets show that IT-DT achieves F1-scores of 0.832 and 0.615, respectively, with better precision than deep learning baselines such as TranAD. Computational profiling indicates that the analytical approach requires minimal memory and provides approximately a 600x inference speedup over transformer-based methods on CPU hardware. This makes the framework suitable for resource-constrained industrial edge controllers without GPU acceleration.

cs.CR↗

arXiv2025

Semi-Supervised Supply Chain Fraud Detection with Unsupervised Pre-Filtering

Detecting fraud in modern supply chains is a growing challenge, driven by the complexity of global networks and the scarcity of labeled data. Traditional detection methods often struggle with class imbalance and limited supervision, reducing their effectiveness in real-world applications. This paper proposes a novel two-phase learning framework to address these challenges. In the first phase, the Isolation Forest algorithm performs unsupervised anomaly detection to identify potential fraud cases and reduce the volume of data requiring further analysis. In the second phase, a self-training Support Vector Machine (SVM) refines the predictions using both labeled and high-confidence pseudo-labeled samples, enabling robust semi-supervised learning. The proposed method is evaluated on the DataCo Smart Supply Chain Dataset, a comprehensive real-world supply chain dataset with fraud indicators. It achieves an F1-score of 0.817 while maintaining a false positive rate below 3.0%. These results demonstrate the effectiveness and efficiency of combining unsupervised pre-filtering with semi-supervised refinement for supply chain fraud detection under real-world constraints, though we acknowledge limitations regarding concept drift and the need for comparison with deep learning approaches.

cs.LG↗