SecureAI-IoT: A Novel Framework for Enhanced Security and Privacy in AI-Powered Internet of Things Systems

N RAJA KUMAR

doi:10.36948/ijfmr.2026.v08i02.70801

SecureAI-IoT: A Novel Framework for Enhanced Security and Privacy in AI-Powered Internet of Things Systems

Author(s)	Dr. N RAJA KUMAR
Country	India
Abstract	The convergence of Artificial Intelligence (AI) and Internet of Things (IoT) has revolutionized various domains, from smart healthcare to industrial automation. However, this integration introduces significant security and privacy challenges that threaten the widespread adoption of AI-IoT systems. Traditional security mechanisms are insufficient to address the unique vulnerabilities arising from distributed IoT architectures, heterogeneous device capabilities, and AI model susceptibilities. This paper proposes SecureAI-IoT, a comprehensive security framework that integrates blockchain-based authentication, federated learning for privacy-preserving AI, edge-based intrusion detection, and homomorphic encryption for secure data processing. The framework addresses critical security concerns including unauthorized access, data breaches, adversarial attacks on AI models, and privacy violations in IoT ecosystems. We present a layered architecture comprising device-level security, network-level protection, and application-level safeguards. Experimental evaluation demonstrates that SecureAI-IoT achieves 97.8% threat detection accuracy with minimal latency overhead (average 12ms), making it suitable for real-time IoT applications. The framework reduces unauthorized access attempts by 94.3% while maintaining data privacy through decentralized federated learning. Comparative analysis with existing approaches shows superior performance in terms of security metrics, computational efficiency, and scalability. Our contributions include: (1) a novel multi-layered security architecture, (2) integration of blockchain with federated learning, (3) lightweight cryptographic protocols for resource-constrained devices, and (4) comprehensive threat modeling for AI-IoT systems. The proposed framework provides a practical solution for deploying secure and privacy-preserving AI-powered IoT systems across diverse application domains.
Keywords	Keywords: Internet of Things, Artificial Intelligence, Security, Privacy, Blockchain, Federated Learning, Edge Computing, Intrusion Detection, Homomorphic Encryption, AI-IoT Framework
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-03-19
DOI	https://doi.org/10.36948/ijfmr.2026.v08i02.70801

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SecureAI-IoT: A Novel Framework for Enhanced Security and Privacy in AI-Powered Internet of Things Systems

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