Detecting distributed denial of service (DDoS) in SD-IoT environment with enhanced firefly algorithm and convolution neural network

Abstract

The Internet of Things (IoT) devices have become part of today’s human life and the amplified use of smart phones as well as number of IoT devices in everyday life has made network security more important. These IoT devices are less secure and often abandoned making them a easy target of DDoS attacks which caused by extreme network packet flow threaten vital network services. The DoS is a single-server attack in which the DDoS is a multi-server attack and IoT devices are managed through Software-defined networks (SDN). This research is proposed to improve the Firefly method in optimizing the Convolution neural network (CNN) for detecting DDoS attacks in Software Defined-IoT (SD-IoT). The suggested SD-IoT framework has been structured into three layers, namely application layer, control layer and user infrastructure layer. The SDN-IoT architecture layer model, Security apps identify DDoS attack in Application layer C-DAD attack detection. The second layer manages the SD-IoT network with the SDNWISE controller and the IoT controller. The infrastructure layer contains SOFS (Sensor Open Flow Switch) and IoT devices. However, this proposal has considered each firefly as a single hyperparameter and by updating the position of firefly periodically results in the reduction of the search iteration. Hence, the proposed model is evaluated by Root mean square and used for measuring the training accuracy of the proposed model. DDoS attacks use a backtracking technique to pinpoint the source, which increases response time. Four CNN models by varying layers and parameters has implemented to improve DDoS detection yielded results with 98% accuracy.