Advanced Metering Infrastructure (AMI) networks in smart grids face evolving cybersecurity threats, with Distributed Denial of Service (DDoS) attacks posing critical risks to network availability and operational integrity. This paper presents a comprehensive evaluation of deep learning architectures for DDoS attack detection in AMI environments, focusing on Convolutional Neural Network (CNN), CNN-Long Short-Term Memory (CNN-LSTM), and CNN-Gated Recurrent Unit (CNN-GRU) hybrid approaches. Using a dataset of 72,007 AMI samples enhanced with realistic DDoS attack simulations covering six attack categories, we implemented extensive feature engineering generating 3,398 features optimized to 75 discriminative indicators. The methodology incorporates synthetic attack generation maintaining 30% attack ratio with stratified data partitioning for robust evaluation. The results gotten from experiments demonstrate that the CNN architecture achieves excellent performance with 86.52% accuracy, 91.08% precision, and 85.73% F1-score, requiring only 23.7 minutes training time and 291,841 parameters. The CNN-LSTM hybrid shows competitive performance with 85.82% accuracy and enhanced temporal modeling capabilities. Both architectures significantly outperform the more complex CNN-GRU variant, establishing CNN-based approaches as optimal for AMI security applications. This research contributes evidence-based architectural guidance for AMI threat detection systems, demonstrating practical deployment solutions that balance detection accuracy with computational efficiency for real-world utility environments.
S. Bassey, P. Asuquo, Victor Anaga et al.· E3S Web of Conferences· 0 citations
This paper presents a comprehensive multi-modal artificial intelligence framework for the prediction of disease from electronic health records that integrates ClinicalBERT natural language processing with graph neural networks, temporal modeling and explainability analysis. Using Synthea synthetic EHR dat with SNOMED CT codes from 1,171 patients, our approach combines semantic understanding of clinical narratives with structural modeling of patient-disease-treatment relationships. The system achieves predictive performance with macro-averaged F1 score of 0.4512 and AUC of 0.9071 across six chronic conditions, demonstrating outstanding results for diabetes (F1=0.900) and hypertension (F1=0.949). Novel contributions include temporal progression forecasting over 12-month periods using LSTM-Transformer hybrid architecture and comprehensive explainability framework providing gradient-based feature importance analysis and automated clinical reasoning generation. The frameworks successfully validates synthetic EHR data utility for privacy-preserving healthcare AI development while addressing critical requirements necessary for clinical decision support system.
U. Luke, P. Asuquo, Victor Anaga et al.· E3S Web of Conferences· 0 citations
Comparative evaluation against existing machine learning and deep learning approaches indicates that the proposed framework achieves competitive accuracy while maintaining deployment-oriented processing speeds, suggesting that the CNN-GRU model is well-suited for SDN security monitoring under controlled experimental conditions.
Victor Anaga, B. Stephen, E. Adediji et al.· E3S Web of Conferences· 0 citations