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