The reliability of phishing Uniform Resource Locator (URL) detectors under adversarial URL rewriting, domain shift, and explanation instability remains insufficiently understood. This study proposes an integrated robustness evaluation protocol for URL-based phishing detection, which integrates structured adversarial perturbation, unseen attack-family generalization, compositional attack effects, explanation stability, and external vulnerability transfer.
The protocol tests four representative model families: Logistic Regression, XGBoost, CharCNN, and BERT-base, using 235,370 validated URLs from PHIUSIIL, consisting of 100,520 phishing and 134,850 benign URLs, along with 49,121 PhishTank-validated phishing URLs for external validation.
All models performed well on the clean test sets, ranging from 0.9962 to 0.9984, but their robustness decreased substantially under realistic URL mutations. Subdomain injection degraded the strong performance of Logistic Regression, XGBoost, and CharCNN to around 0.432, indicating collapse to the phishing-prevalence floor. BERT was highly susceptible to homoglyph, padding, and path-based perturbations. Leave-one-family-out evaluation also showed poor transfer to unseen subdomain attacks for both Logistic Regression and XGBoost, with Robustness Degradation Index values of 0.535 and 0.565, respectively. Explanation stability also suffered, with SHAP top-K Jaccard similarity dropping to 0.526-0.535 under subdomain perturbation.
These results provide a solid benchmark for evaluating robustness-aware phishing URL detection for achieving deployable reliability under realistic adversarial and non-IID settings.
Tanvir Ahamed, Shawon Chakrabarty Kakon, Fahmid Al Farid et al.· Frontiers of Computer Scienc...· 0 citations
Smart fault diagnosis is a crucial component of modern industrial systems, offering early detection of machine conditions to prevent costly breakdowns and safety issues. While state-of-the-art deep learning models are capable of near-perfect classification on controlled laboratory data, they often perform poorly in practice because of noisy environments, class imbalance and distribution shifts across operating conditions. This paper addresses these challenges by re-formulating industrial acoustic monitoring as a supervised image classification problem with superlet time-frequency representations, which capture both the transient dynamics and phase-envelope. An efficient CNN-Transformer hybrid model is proposed by placing a lightweight Transformer encoder on an ImageNet-pretrained ResNet-50 backbone with spatial attention gating to learn both local and global spectro-temporal features. The proposed architecture is lightweight, with 25.6M parameters and 4.1G FLOPs, supporting efficient resource usage. An asymmetric focal contrastive learning strategy is also proposed to improve anomaly discrimination under extreme imbalance. Experimental results on the MIMII dataset demonstrate that the proposed framework achieves 94.82% accuracy, 91.08% balanced accuracy, 91.72% F1-score, and 0.9758 ROC-AUC under a strict group-aware evaluation protocol, while requiring only 25.6M parameters, 4.1G FLOPs, and 4.5 ms inference time per sample. In cross-domain evaluation on the CWRU dataset, the model further achieves a zero-shot AUC of 0.8886 and a fine-tuned AUC of 1.0000 within three epochs. These results validate that the proposed framework provides an effective, robust, and generalizable solution for industrial fault diagnosis.
S. Anik, Md. Ehsanul Haque, Fahmid Al Farid et al.· Scientific Reports· 0 citations
A hybrid SDN-based IDS framework that integrates Generative Adversarial Networks (GANs) to handle imbalanced datasets, one-way ANOVA and Genetic Algorithm for feature selection, baseline classifier optimization using Grid Search and Explainable AI techniques to achieve robust, accurate, and interpretable intrusion detection.
Md. Tamim Hasan Saykat, Md. Ehsanul Haque, Fahmid Al Farid et al.· Scientific Reports· 0 citations
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