Detection of distributed denial of service attack using enhanced adaptive deep dilated ensemble with hybrid meta‐heuristic approach

Abstract

AbstractThe biggest firms throughout the world now are the ones that offer services in the cloud. One of the top problems for cloud users (CUs) and cloud service providers (CSPs) is the availability of cloud‐based services whenever needed. A distributed denial of service (DDoS) assault has been a significant threat to the security of the system in recent years. DDoS defense and detection of these attacks have become hot topics of research for academia and business. However, most approaches cannot accomplish efficient detection outcomes with few false alarms. As a result, minimizing the consequences of DDoS attacks allows CSPs to offer CUs high‐quality services. In the cloud sector, a collective deep structured algorithm is suggested to identify DDoS attacks successfully. The recommended method contains several stages: data acquisition, pre‐processing, optimal feature detection, and selection. The first step is the acquisition of data using the help of publicly available sources. Further, the input data undergoes preprocessing. Consequently, the optimal weighted feature selection takes place on the preprocessed data, where the optimization is done with the aid of the Hybrid Border Collie and Dragonfly Algorithm (HBCDA). Finally, DDoS attack detection is achieved via a novel method known as adaptive deep dilated ensemble (ADDE), which includes, one‐dimensional convolutional neural network (1DCNN), deep temporal convolutional neural network (DTCNN), recurrent neural network (RNN), and bidirectional long short‐term memory (Bi‐LSTM). For the attainment of optimal results, the parameter tuning is accomplished by using the HBCDA approach. The detection outcome is computed by using the fuzzy ranking mechanism. The validation is done for the suggested method model, and its corresponding findings are validated with conventional techniques. Hence, the suggested approach outperforms the detection performance and ensures more efficiency than traditional approaches.