A Hybrid Machine Learning and Blockchain Framework for IoT DDoS Mitigation

The explosive expansion of the Internet of Things (IoT) systems has increased the imperative to have strong and robust solutions to cyber Security, especially to curtail Distributed Denial of Service (DDoS) attacks, which can cripple critical infrastructure. The proposed framework presented in the current paper is a new hybrid scheme that induces deep learning-based traffic classification and blockchain-enabled mitigation to make intelligent, decentralized, and real-time DDoS countermeasures in an IoT network. The proposed model fuses the extracted deep features with statistical features and trains them by using traditional machine-learning algorithms, which makes them more accurate in detection than statistical features alone, based on the Convolutional Neural Network (CNN) architecture, which can extract deep features. A permissioned blockchain will be included to record the threat cases immutably and automatically execute mitigation measures through smart contracts to provide transparency and resilience. When tested on two test sets, BoT-IoT and IoT-23, the framework obtains a maximum F1-score at 97.5 percent and only a 1.8 percent false positive rate, which compares favorably to other solutions regarding effectiveness and the amount of time required to respond. Our findings support the feasibility of our method as an extensible and secure paradigm of next-generation IoT security, which has constrictive utility in mission-critical or resource-constrained settings. The work is a substantial milestone in autonomous and trustful mitigation against DDoS attacks through intelligent learning and decentralized enforcement.