Phishing attacks have emerged as a significant cybersecurity threat to e-commerce platforms, targeting users to obtain sensitive information such as login credentials, banking details, and financial records. Existing machine learning and deep learning approaches often suffer from limitations related to redundant features, inefficient feature representation, and reduced generalization capability in dynamic phishing environments. To address these challenges, this paper proposes an intelligent data mining model for phishing detection in e-commerce platforms. The proposed framework integrates feature selection and embedding-based representation learning to capture meaningful patterns from phishing-related data and improve classification performance. Experimental evaluation demonstrates strong detection capability, achieving 93.93% accuracy with balanced precision, recall, and F1-score, along with an AUC of 0.984. The results confirm the robustness, reliability, and practical applicability of the proposed framework for enhancing security and trust in modern e-commerce systems.
Early academic risk prediction aims to identify at-risk students from limited early-course behavioral observations and to provide evidence for timely educational support. Existing methods usually overlook the discrepancy between a student's current state and their historical trajectory, resulting in insufficient capture of early signals and limited interpretability. To address this issue, the authors propose EduRiskNet, an interpretable framework for early academic risk prediction. EduRiskNet integrates multiperspective behavioral encoding, semantic state modeling, and state deviation quantification to capture early risk formation signals in learning trajectories. A dual-scale dynamic module further combines long-term trajectory patterns with short-term anomalies for early risk identification. Experiments on two public datasets show that EduRiskNet outperforms the tested baselines. The model also organizes risk scores, state-deviation evidence, and dominant behavioral factors into structured information for decision-support priorities.
With the rise of location-based services applications, research on point-of-interest (POI) recommendation has gained significant attention. Conventional federated POI recommendation studies suffer from insufficient prediction accuracy. This study introduces a novel POI federated recommendation method: Spatio-Temporal self-Attention Recommendation for POI (STAR-POI). STAR-POI employs a spatiotemporal self-attention network to learn the spatial and temporal relationships between discontinuous check-ins, a projection enhancement gradient similarity index to cluster clients with their model updates. Model accuracy comparisons showed that STAR-POI outperforms three baseline models across all metrics, as it can capture more nuanced user behaviors. Further, variation analysis of model accuracy revealed the method’s saturation points concerning the number of clients, evaluation metrics, data distribution in the study area, and the projection enhancement factor λ. These insights can guide the selection of optimal parameters for model training and POI recommendation.
Online learning platforms accumulate student-question records for learning analytics, early warning, and institutional decision support. Mining these records requires accurate next-response prediction and stable modeling of temporal-relational patterns among items and knowledge components. Existing knowledge tracing methods use static structures or separate temporal and relational modeling, limiting their ability to capture evolving dependencies and causing relational drift. To address this issue, this paper proposes the Cross-Temporal Relational Alignment Network (CTRA-Net), a framework for joint sequential and dynamic structural modeling. CTRA-Net uses time-conditioned graph attention to reconstruct knowledge relations under the current learner state and a memory bank to preserve historical prototypes for cross-stage alignment. Student-level five-fold cross-validation on six public datasets shows that CTRA-Net outperforms sequential, graph-based, spatiotemporal, memory-based, and semantic-enhanced baselines. Drift, cold-start, and efficiency analyses show better stability with deployable cost.
Phishing attacks remain one of the primary cybersecurity threats because attackers exploit users through deceptive URLs focusing primarily on online banking. The research undertakes a comparative assessment between deep learning and machine learning detection algorithms applied to find phishing URLs focused on banking clients. The research implemented Convolution Neural Network (CNN), Long Short-Term Memory (LSTM) and alongside Logistic Regression (LR) and Random Forest (RF) and Support Vector Machine (SVM) and XGBoost as independent training models for evaluation. The CNN model proved its superiority in identifying complicated patterns of malicious URLs because it reached 99% accuracy during testing and training. Public performance standards enabled the assessment of robust model behavior through an extensive evaluation process. Upcoming research will investigate instant threat identification techniques as well as extra feature integration strategies to boost system performance for protective measures in changing security threats.
