Foggier skies, clearer clouds: A real-time IoT-DDoS attack mitigation framework in fog-assisted software-defined cyber-physical systems

Abstract

IntroductionThe convergence of the Internet of Anything (IoX), software-defined communication layer, and sophisticated cloud platform has steered the dawn of the fourth industrial revolution era (Industry 4.0). The technological leap forward has given shelter to the umbrella of cyber-physical systems such as Smart Grids, Agriculture, and Healthcare. The exponential growth of vulnerable spots (the Internet of Things) in multi-layer cyber-physical systems is involuntarily contributing to malignant IoT-generated denial-of-service attacks. The IoT devices manifest a high degree of diversity in traffic patterns, and a single dataset is insufficient to cover fiducial attack scenarios. In addition, the research community is pressing to overcome other roadblocks, such as an optimal architecture for solution efficacy, performance issues, and the need for comprehensive validation of the proposed solutions. MethodsThe paper offers a five-stage defense framework for building a mitigation against IoT-based DDoS attacks. The article brings forth an amalgamated dataset concocted from InSDN, BoT-IoT, and UNSW-Sydney datasets and a simulated dataset for IoT-DDoS to the research community. The paper employs a multi-stage Stack-Ensembled framework at the heart of the solution pillared on physical devices' behavioral attributes, resulting in a universal defense approach. The experiment leverages fog computing with distributed computational nodes to reduce response latency. Furthermore, the paper implements attack-detection-as-a-service on top of the Docker framework for a cost-effective, reusable, and portable framework. The novel design of the framework presents a light mitigation scheme to the SDN controller, ensuring a negligible impact on the controller's performance. Results and DiscussionThe hand-crafted feature selection process reduced the features by 80%, demonstrating a high accuracy of 99.99% with benchmark datasets, 98.84% accuracy in the simulation environment, and collateral damage of 1.52%. The experiment observes encouraging values for vital performance parameters that researchers often miss to discuss. Furthermore, the paper thoroughly analyzes IoT-DDoS mitigation framework performance parameters.