An efficient metaheuristic algorithm based feature selection and recurrent neural network for DoS attack detection in cloud computing environment - SaiSindhuTheja, Reddy; Shyam, Gopal K. (Applied soft computing, 2021)
Detection of Denial of Service (DoS) attack is one of the most critical issues in cloud computing. The attack detection framework is very complex due to the nonlinear thought of interruption activities, unusual conduct of systems traffic, and many attributes in the issue space. This paper proposes an efficient DoS attack detection system that uses the Oppositional Crow Search Algorithm (OCSA), which integrates the Crow Search Algorithm (CSA) and Opposition Based Learning (OBL) method to address such type of issues. The proposed system consists of two stages viz. selection of features using OCSA and classification using Recurrent Neural Network (RNN) classifier. The essential features are selected using the OCSA algorithm and then given to RNN classifier. In the subsequent testing process, incoming data is classified using the RNN classifier. It ensures the separation of standard data (saved in cloud) and the removal of compromised data Using the benchmark data set, the results of experimental evaluation demonstrate that the proposed technique outperforms the other conventional methods by 98.18%, 95.13%, 93.56%, and 94.12% in terms of Precision, Recall, F-Measure, and Accuracy respectively. Further, the proposed work outperforms existing works by 3% on an average for all the metrics used. •Introducing a new algorithm named Oppositional Crow Search Algorithm (OCSA).•The proposed OCSA is validated on KDD cup 99 dataset.•Feature selection is performed through OCSA algorithm.•Further classification is performed through Recurrent Neural Network (RNN).•OCSA hits with Precision-98.18%, Recall-95.13%, F-measure-93.56% & Accuracy-94.12%.