Advancing AI-Driven Threat Detection in IoT Ecosystems: Addressing Scalability, Resource Constraints, and Real-Time Adaptability
Krishna Chaganti
Abstract
The rapid expansion of Internet of Things (IoT) ecosystems has revolutionized industries by enabling real-time data exchange and interconnected operations. However, this growth presents significant security challenges, including scalability, resource constraints, and the need for real-time adaptability to evolving cyber threats. To address these issues, this study proposes an innovative, AI-driven framework that integrates lightweight intrusion detection systems (IDS), blockchain-based authentication, and edge computing. This qualitative research synthesizes peer-reviewed literature to identify existing gaps and design a multi-layered security solution tailored for resource-constrained IoT environments. The proposed framework enhances scalability by leveraging decentralized blockchain systems and edge computing for distributed data processing. Lightweight AI algorithms are employed to optimize resource efficiency and ensure real-time adaptability. Analytical comparisons with traditional security models demonstrate the framework's superiority in mitigating IoT vulnerabilities while maintaining computational feasibility. This study contributes to the theoretical and practical understanding of IoT security, offering a scalable, resource-efficient, and adaptive solution for emerging threats.