The convergence of the Internet of Things (IoT) with Web3 ecosystems introduces new opportunities for automation, trust, and decentralized coordination. However, the same decentralized nature also amplifies security vulnerabilities, as conventional centralized intrusion detection and response systems are unable to provide real-time, tamper-proof protection at scale. This paper presents the Smart Contract-Based Automated Response System (SC-ARS), a novel framework that integrates blockchain smart contracts, machine learning (ML)-based anomaly detection, and automated mitigation policies. SC-ARS leverages lightweight consensus mechanisms and decentralized storage to ensure resilience against single points of failure, while smart contracts provide transparent and auditable enforcement of security actions. The ML pipeline, implemented using Random Forest, XGBoost, and LSTM models, is trained on benchmark datasets (NSL-KDD and CICIDS2017) to enable accurate anomaly detection. Experimental evaluation demonstrates up to 95% detection accuracy, a 50% reduction in response latency, and scalability to over 100,000 IoT devices without performance degradation. These results highlight the suitability of SC-ARS for deployment in smart cities, industrial IoT, and decentralized critical infrastructures where trust, transparency, and real-time responsiveness are essential.
S. Bassey, B. Stephen, Emediong Bassey Obot et al.· E3S Web of Conferences· 0 citations
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
To address the critical global challenge of microplastic pollution and the limitations of alignment-dependent genomic tools in analyzing fragmented environmental data, this study introduces an alignment-free computational framework for identifying microbial bioremediation potential. We established a standardized, multi-domain dataset encompassing Bacteria, Protists, Archaea, and Fungi, integrating metadata on polymer interactions to fill existing data gaps. Utilizing tetranucleotide frequency patterns (k=4), we developed a novel analysis method to isolate predictive genomic signatures, identifying C-rich motifs such as ‘CCCC’ as primary indicators of degradation capability.A statistical scoring model was subsequently implemented to rank candidate taxa, effectively prioritizing high-value organisms from complex metagenomes derived from plastic-associated microbial assemblages. Our approach demonstrates significantly reduced computational time and more sensitive than the older methods, identifying potential degraders that alignment techniques often miss. It connects the theoretical side of detecting functional genes with real-world environmental checks, creating a flexible tool to speed up finding new microbes for tackling plastic waste and restoring ecosystems.
U. Luke, Emem Okon Abang, Etimbuk Daniel Akpan et al.· E3S Web of Conferences· 0 citations
Efficient resource allocation in Cloud computing necessitates the capability to accurately predict and adapt to Cloud workloads with both precision and flexibility. Nevertheless, current approaches struggle to predict Cloud workloads with high dimensionality. This leads to inefficient resource utilization and the failure to meet service level agreements (SLAs). For these important challenges to be addressed, a dynamic recurrent neural network is proposed to accurately predict workloads. The proposed algorithm integrates an auto-encoder to effectively extract representations from the original workload data with high dimensionality to enable adaptive and accurate predictions for highly variable workloads. Experiments are conducted using Google Cloud data centers to validate the effectiveness and adaptability of the proposed scheme across various workload types and prediction lengths. The proposed scheme achieves 81.48% lower MSE and maintains an MSE of 0.005 to 0.015 compared to traditional Deep learning-based methods with up to 0.027 for Cloud workloads. This is achieved by successfully predicting continuous resource metrics (CPU/Memory/GPU utilization) rather than categorical labels, demonstrating strong adaptability across various prediction lengths.
Okore Kalu, Chijioke Okafor, P. Asuquo et al.· E3S Web of Conferences· 0 citations
The integration of Internet of Things (IoT) systems with blockchain-based security mechanisms offers improved trust, auditability, and decentralization, but introduces significant challenges related to interoperability, transaction latency, and energy consumption at the network edge. Most existing blockchain-assisted intrusion detection systems (IDS) are limited to single-ledger deployments and rely on computationally intensive machine-learning inference, which restricts their applicability in resource-constrained IoT environments. Presented in the work is a cross-chain and energy-efficient intrusion detection framework that combines lightweight machine-learning–based anomaly detection with smart-contract–driven verification and interoperable blockchain communication. Intrusion detection models are optimized using pruning and INT8 quantization to reduce inference overhead on IoT gateways, while verified intrusion alerts are securely propagated across heterogeneous blockchain platforms through cross-chain messaging protocols. Smart contracts automate alert validation and response actions, enabling coordinated defense across multiple ledgers. Experimental evaluation using CICIDS2017 and BoT-IoT datasets, physical IoT gateway hardware, and a multi-chain blockchain testbed demonstrates that the proposed framework achieves a detection accuracy of 97.4%, reduces inference energy consumption by up to 56%, and improves alert propagation latency by 42% compared to single-chain baselines. These results indicate that decentralized, interoperable, and energy-aware intrusion detection is feasible for large-scale IoT deployments.
S. Bassey, Emmanuel Udoh, B. Stephen et al.· E3S Web of Conferences· 0 citations
The Internet of Medical Things (IoMT) is transforming healthcare delivery, but brings significant security and privacy challenges due to the diverse range of devices, sensitive patient data, and real-time operation requirements. Existing Intrusion Detection Systems (IDS) have improved detection and privacy through approaches such as federated learning and blockchain, yet they focus primarily on network-level attacks, overlooking device-level attacks which is the primary source of data leakage, device unavailability, and model poisoning in federated learning (FL)-based approaches. For instance, Bring Your Own Device (BYOD) introduces heterogeneity and Non-Independent and Identically Distributed data (non-IID) distributions that affect the performance of conventional FL approaches. We therefore propose an intelligent, lightweight Tiny LSTM–GRU hybrid IDS on the edge to monitor device-generated behavioral patterns in real time, with minimal computational and energy overhead. To preserve privacy and handle the issue of non-IID data across heterogeneous IoMT devices, we propose an adaptive FedProx-based weighted federated learning framework. Our proposed framework achieves an overall accuracy of 99.1% on the edge with latency between 1.8ms per sample, with a mean global accuracy of 99.4% and global loss of 0.037 during convergence, making it highly suitable for real-world IoMT deployments.
Emmanuel Udok, B. Stephen, U. Luke 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
The results show a success in implementing a real time, scalable, privacy-preserving, and adaptive IDS in large-scale IoT deployments through intelligent workload distribution between edge and cloud layers.
Chidera Winifred John, Eduediuyai Ekerete Dan, P. Asuquo et al.· E3S Web of Conferences· 0 citations