Efficient Intelligent Intrusion Detection System for Heterogeneous Internet of Things (HetIoT)

Moving towards a more digital and intelligent world equipped with internet-of-thing (IoT) devices creates many security issues. A distributed denial of service (DDoS) attack is one of the most formidable and challenging security threats that has taken hold with the emergence of the heterogeneous IoT (HetIoT). The massive DDoS attacks have exhibited their impact by continuously destroying a variety of infrastructures, resulting in huge losses, and endangering the overall availability of the digital world. The emphasis of this research is to identify and mitigate various DDoS attacks for HetIoT. The research proposes an intelligent intrusion detection system (IDS) using a convolutional neural network (CNN), i.e., HetIoT-CNN IDS, a novel deep learning-based convolutional neural network for the HetIoT environment. The proposed intelligent IDS successfully identifies and mitigates various DDoS attacks in the HetIoT infrastructure. The feasibility of the new proposed HetIoT-CNN IDS is assessed by considering binary and multi-class (8- and 13-classes) classification. The performance of the proposed intelligent IDS is compared with two state-of-the-art deep learning approaches for HetIoT, and the results reveal that the proposed HetIoT-CNN IDS outperforms it. The proposed HetIoT-CNN IDS successfully identifies various DDoS attacks with an accuracy rate of 99.75% for binary classes, 99.95% for 8-classes, and 99.99% for 13-classes. The work also compares the individual accuracy of binary classes, 8-classes, and 13-classes with state-of-the-art work.