Intelligent Self-reliant Cyber-Attacks Detection and Classification System for IoT Communication Using Deep Convolutional Neural Network

Internet of Things (IoT) is a promising profound technology with tremendous expansion and effect. However, IoT infrastructures are vulnerable to cyber-attacks due to the constraints in computation, storage, and communication capacity for the endpoint devices such as thermostat, home appliance, etc. It was reported that 99% of the cyber-attacks are developed by slightly mutating previously known attacks to generate a new attack tending to be handled as a benign traffic through the IoT network. In this research, we developed a new intelligent self-reliant system that can detect mutations of IoT cyber-attacks using deep convolutional neural network (CNN) leveraging the power of CUDA based Nvidia-Quad GPUs for parallel computation and processing. Specifically, the proposed system is composed of three subsystems: Feature Engineering subsystem, Feature Learning subsystem and Traffic classification subsystem. All subsystems are developed, verified, integrated, and validated in this research. To evaluate the developed system, we employed the NSL-KDD dataset which includes all the key attacks in the IoT computing. The simulation results showed a superior attacks’ classification accuracy over the state-of-art machine learning based intrusion detection systems employing similar dataset. The obtained results showed more than 99.3% and 98.2% of attacks’ classification accuracy for both binary-class classifier (normal vs anomaly) and multi-class classifier (five categories) respectively. All development steps and testing and verification results of the developed system are reported in the paper.