DDoS attack prediction using a honey badger optimization algorithm based feature selection and Bi-LSTM in cloud environment

Abstract

Users are provided access to on-demand services through the Internet with the assist of cloud computing. Services can be accessed at any time and from any place. Although delivering useful services, this model remains vulnerable to security problems. The accessibility of cloud resources is affected by Distributed Denial of Service (DDoS) attacks, which also present security risks for cloud computing. Unable to access data from cloud services, various advanced risks such as malware injection, packagingas well as virtual machine escapes and DDoS are developed by the attackers. Recently, numerous models were designed for detecting attacks in the cloud, but still they lack certain reasons. To alleviate these concerns, this proposed method presented a DDoS attack prediction using a honey badger optimization algorithm based on feature selection and Bi-LSTM in a cloud environment. Input features are gathered from the DDoS attack dataset as the first step in the process. Following this, input features are transmitted into preprocessing steps, including Bayesian and Z-Score normalization. Preprocessed data is sent into the feature selection phasethat employs Honey Badger Optimization (HBO). In this case, the features are chosen by decreasing their MSE to obtain the best feature. Then, optimal features are fed into the Bi-directional Long Short term Memory (Bi-LSTM) classifier for predicting DDoS attacks. The proposed model is also examined using certain existing approaches, including LSTM, DNN, DBN and ANN. When the performance was examined using the existing method, the Bi-LSTM model achieved 97% accuracy, 95% sensitivity, 90% specificity, 3% error, 94% precision and so on. The proposed model is effective at finding DDoS in a cloud environment.