Federated Learning-Based Hybrid Model for Secure Fraud Detection in Distributed Rural Environments (Published)
Accurate and efficient detection of fraudulent financial transactions is essential for ensuring security, trust, and stability in modern digital financial systems. However, this task remains challenging due to highly imbalanced datasets, continuously evolving fraud strategies, heterogeneous data distributions across different financial institutions, and strict privacy constraints in distributed environments. To address these challenges, this research proposes a federated learning-based hybrid deep learning model for fraud detection across distributed rural financial environments. The evaluation was conducted using the Credit Card Fraud Detection Dataset (CCFD), which comprises 284,807 financial transactions with 492 fraudulent transactions. This dataset is severely skewed between valid and fraudulent classes. To mitigate class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied during preprocessing to improve the representation of minority fraud samples and enhance model learning capability. The proposed Pity Beetle Algorithm- driven Federated-tuned Recurrent Neural Networks (PBA-FederatedRNN) model integrates Recurrent Neural Networks (RNNs) for sequential transaction behavior learning and the Pity Beetle Algorithm (PBA) for optimized parameter tuning and faster convergence. Federated learning ensures robust data security and privacy preservation by allowing several financial nodes to cooperatively train a global model without exchanging raw data. The model was implemented using Python with TensorFlow/PyTorch in a distributed simulation environment. According to experimental results, the proposed model outperforms existing centralized and federated approaches with 97.8% recall, 98.0% F1-score, 98.7% accuracy, and 98.2% precision. With all factors considered, the proposed PBA-FederatedRNN model offers a flexible, dependable, and highly efficient fraud detection solution in distributed financial systems that protect privacy.
Keywords: Fraud Detection, distributed systems, federated learning, hybrid model, privacy preservation, rural environments, secure aggregation
An Enhanced Security Framework for IoT Devices through Federated Learning (Published)
The increasing deployment of Internet of Things (IoT) devices across diverse environments has introduced significant security challenges, particularly due to the distributed nature of IoT networks and the vast amount of sensitive data they generate. This research addresses the pressing issue of enhancing IoT security by proposing a decentralized and privacy-preserving approach that integrates federated learning models for intrusion detection. The proposed system leverages TensorFlow, Keras, and TensorFlow Federated libraries, implemented in the Python programming language, to train local models across multiple IoT clients. Each client learns from its own partition of the KDDCup 1999 dataset, a widely recognized benchmark in network intrusion detection. The system was evaluated across key performance metrics including accuracy, detection rate, and classification reliability. Experimental results demonstrated a consistent improvement in model accuracy from 93% and detection rate from 92% over 40 epochs. The distribution of detected attack types such as DDoS, phishing, malware, and ransomware further showcased the system’s practical applicability in heterogeneous IoT environments. This study confirms that federated learning is a viable approach to securing IoT systems, as it supports accurate and scalable threat detection while upholding data privacy. The model not only enhances trust and security but also demonstrates adaptability across various IoT scenarios with constrained computational resources. These scores clearly illustrated the advantage of the federated model in speed and responsiveness.
Keywords: federated learning, intrusion detection, iot security, privacy preservation, ransomware
AI-Driven Security Architecture in Smart Cities: Balancing Safety and Privacy (Published)
Smart cities integrate interconnected technologies to enhance urban living through efficient infrastructure and services, yet this technological evolution introduces significant cybersecurity vulnerabilities that threaten critical urban systems. AI-driven security architectures emerge as sophisticated solutions, utilizing machine learning algorithms and predictive analytics to provide real-time threat detection, automated incident response, and proactive defense mechanisms against cyber-attacks. These intelligent systems process vast amounts of data from sensors, cameras, traffic networks, and utility systems to maintain the integrity and availability of essential urban services. While AI-driven security delivers substantial benefits, including enhanced public safety, service continuity, and economic protection, it raises profound privacy concerns and ethical challenges related to surveillance, algorithmic bias, and data misuse. Implementing privacy-preserving technologies such as federated learning and differential privacy, with transparent governance frameworks and public engagement initiatives, offers pathways to balance security effectiveness with individual rights protection. Future developments in explainable AI, quantum-resistant algorithms, and interdisciplinary collaboration will be crucial for creating equitable and trustworthy AI-driven security systems that serve urban communities while preserving democratic values and social equity.
Keywords: AI-driven security, Cybersecurity, IoT networks, privacy preservation, smart cities