Abstract
The rapid expansion of Internet of Things (IoT) devices has introduced significant security challenges, especially in protecting against Denial of Service (DoS) attacks that can compromise network integrity and disrupt essential services. This research presents an innovative framework integrating Support Vector Machine (SVM), Queue modelling, and Blockchain technology to identify and mitigate DoS attacks in IoT networks. The proposed system employs a multi-layered approach: SVM for attack classification, priority queue modelling for traffic management, and blockchain for secure signature sharing.
The proposed framework effectively distinguishes between normal and malicious traffic patterns, while the priority queue mechanism demonstrates strong performance in managing network congestion and ensuring efficient traffic processing. The system offers the detection and classification of various types of DoS attacks, such as Hypertext Transfer Protocol (HTTP) flood, Transmission Control Protocol (TCP) flood, Synchronize (SYN) flood, and User Datagram Protocol (UDP) flood. MATLAB© simulations are utilized to validate the system's reliability and responsiveness, demonstrating its ability to operate in real-time scenarios. This research contributes to IoT security by presenting a scalable and resource-efficient solution that balances computational demands with robust security measures. At the same time, the integration of blockchain technology enhances trustworthiness and enables decentralized threat mitigation, providing a promising approach for securing future IoT deployments.