Optimized back propagation neural network for DDoS attack detection in the cloud environment

Cloud computing facilitates users with on-demand services over the Internet. Depending on demand, cloud services are frequently utilized as private or public data forums, and the rise in usage has raised security issues. The distributed denial of service (DDoS) attack is one of these security risks that affect cloud computing services. In this work, the optimized back propagation neural network (BPNN) is proposed to detect DDoS attacks in the cloud environment. The proposed optimized BPNN is used artificial plant optimization (APO) algorithm for optimizing the weights and bias of the connections. The proposed APO-BPNN detection system is evaluated using four datasets namely, NSL-KDD, ISCX-IDS 2012, UNSW-NB15, and CIC-IDS 2017. The experiments show that the performance of the proposed APO-BPNN detection system is better than the system based on variant BPNN and state-of-the-art techniques.