DDoS Attack Detection Using Optimized Long Short-Term Based on Partial Opposition-Based Swarm Intelligence Algorithm

The on-demand provision of computing resources as services is known as cloud computing. The distributed denial of service (DDoS) attack is a major security risk that affects cloud services. Because of the computational complexity that must be handled, detecting DDoS attacks is a very difficult operation for cloud computing. Hence, the research workfocuses on developing an efficient classifier model using optimized long short-term memory (LSTM) based on the partial opposition-based elephant herding optimization (POEHO) method called POEHO-LSTM for detecting DDoS attacks. The experimental findings showed that, for two DDoS datasets-NSL-KDD and ISCXIDS-2012-the suggested POEHO-LSTM had produced exceptional results.