Development of an Intelligent IOT Attacks Detection System Using Non-Traditional Machine Learning (NML) Techniques
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 have been completely developed, verified, integrated, and validated in this dissertation. To evaluate the proposed system, we have employed the NSL-KDD dataset which includes all the key attacks in the IoT computing. Eventually, the simulation results showed a superior classification accuracy figures over the state-of-art machine learning based intrusion detection systems employing similar dataset with more than 99.3% and 98.2% of classification accuracy for both binary-class classifier (normal vs anomaly) and multi-class classifier (five categories) respectively.
Qasem Abu Al-Haija,
"Development of an Intelligent IOT Attacks Detection System Using Non-Traditional Machine Learning (NML) Techniques"
ETD Collection for Tennessee State University.