Mobile banking has been experiencing unsustainable growth in the last ten years, as the volume of digital payments worldwide is currently more than USD 8.49 trillion and is estimated to be more than USD 20 trillion by 2026. At the same time, fraudulent attacks have been directed toward mobile banking platforms to a significant extent, causing losses amounting to USD 485.6 billion worldwide alone in 2023. The current fraud detection architectures have been heavily based on a centralized cloud-based model which is inherently associated with a latency delay of between 200 to 800 milliseconds that introduces a time delay that advanced attackers can use to transact fraudulent transactions before the defensive countermeasures are activated. The paper will suggest a new Edge-to-Cloud Collaborative Fraud Detection (EC-CFD) model that allocates inference workloads to three hierarchical levels. The structure is a combination of differential privacy, mutual authentication, and federated learning to allow joints in improving the model without having to centralize sensitive financial information. The feature engineering has 18 dimensions which include transaction statistics, geo-location anomalies, device fingerprinting and behavioral biometrics. Synthetic dataset of 2.4 million transactions, experimented on with samples of PaySim and IEEE-CIS benchmarks on fraud detection methods to provide realistic behavioural diversification. The findings indicate that the suggested EC-CFD model is characterized by a 98.7 % detection rate of fraud, where the F1-score is 0.974 and the AUC-ROC value is 0.992. It is important to note that the hierarchical structure allows accomplishing 87.2% raw transactions at the edge level and achieves a mean detection latency of 8.3 ms. The proposed framework lowers false positives by 31.4 per cent, the average end-to-end latency by 73, and could be extended to support 50,000 active users without lowered performance, which proves the effectiveness of hierarchical edge intelligence as the primary paradigm of next-generation financial cybersecurity.
Himani Fnu, Harshendra Gite, Virendra Singh Chawra et al.· International Journal of Dat...· 0 citations
Wireless Sensor Networks (WSNs) have become indispensable components of modern cyber-physical systems, supporting healthcare, industrial automation, agriculture, environmental monitoring, and military surveillance. However, their limited computational resources, open wireless communication channels, and unattended long-term deployments expose them to diverse cyberattacks, whilst conventional security mechanisms remain inadequate for such constrained environments. This study aims to develop and evaluate an efficient Machine Learning-based framework for real-time anomaly detection and multi-class attack classification in WSNs. The objective is to enhance network security, maintain detection accuracy, and enable practical deployment on low-resource sensor hardware. Seven supervised and unsupervised learning models, including Random Forest, Support Vector Machine, XGBoost, LSTM, Isolation Forest, Autoencoder, and k-Nearest Neighbour, were assessed using three benchmark datasets: NSL-KDD, UNSW-NB15, and WSN-DS. A tailored feature engineering process selected the most informative network, temporal, and protocol attributes, followed by Bayesianoptimized ensemble learning. The proposed soft-voting ensemble outperformed individual models and achieved highly accurate intrusion detection with strong classification consistency and minimal false alarm rates. Cross-dataset evaluation confirmed robust generalization, while hardware profiling demonstrated low memory usage and fast inference, making the framework feasible for real-time embedded WSN deployment. This research provides a scalable and practical intelligent security solution for WSN environments. Its originality lies in combining high detection performance, lightweight deployment capability, and adversarial robustness analysis, offering significant value for securing future smart infrastructure and resource-constrained IoT systems.
Qasim M. Zainel, Adnan Yousif Dawod, M. Abdulqader et al.· Journal of Cyber Security an...· 0 citations
This study suggests a framework for cybersecurity auditing of smart grid infrastructure, which is based on the concept of risk and the use of Explainable Artificial Intelligence (XAI) to produce transparent, prioritized and audit-ready security evidence. The information from public smart grid cybersecurity events was mapped to event labels, asset classes, security-control status, compliance indicators, and cyber-physical impact variables, which were then used to create audit-relevant records. Attack likelihood estimates were made using machine learning models. The attack likelihood, asset criticality, control deficiency score, compliance condition and operational impact were all added together to calculate the final audit risk score. Explainability was used as a technique to identify the most important features that affected each audit decision by applying the SHAP method. The proposed framework achieved 96.38% accuracy, 96.51% precision, 96.38% recall, 96.42% F1-score, and 0.996 ROC-AUC. The results of the ablation showed that the inclusion of the risk component and the XAI component resulted in an improvement in the risk ranking, audit traceability and explanation consistency. The framework translates the cybersecurity detection results into an understandable audit decision, enabling risk-based remediation, compliance review, and an understandable smart grid cybersecurity governance.
Udit Mamodiya, I. Kishor, Hastimal Jangid et al.· Journal of Cyber Security an...· 0 citations
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