A better and fast cloud intrusion detection system using improved squirrel search algorithm and modified deep belief network

Utilizing the cloud environment is one of the most preferable option in every information technology (IT) organization for running its business due to its flexible nature of services for its users. Cloud computing is vulnerable to various types of known and unknown attacks due to its distributed nature and open architecture. Hence, privacy and security is a primary concern of the cloud computing environment. A lot of machine learning approaches are utilized to improve the accuracy of Intrusion detection system (IDS) but dealing with redundant and non-relevant datasets with a large number of attributes (multi-dimensional) is still a problem. In this study, better and fast IDS has been proposed to detect anomaly for the cloud network environment which uses Improved squirrel search algorithm (ISSA) and Modified-deep belief network (MDBN) on the UNSW-NB15 dataset. ISSA extracts the relevant features from a set of features to deal with network traffic data of high dimension. It selects the relevant and optimal feature subset to be utilized for the training process and testing process. Meanwhile, a MDBN is introduced which uses the extracted features for anomaly detection for binary class classification and multi class classification. MDBN also helps to deal with the unbalanced nature of the dataset. The proposed IDS model has been evaluated based on the statistical measures namely accuracy, false alarm rate (FAR) and execution time, which are the popular parameters for evaluation of an IDS model. The proposed ISSA-MDBN model for IDS reduces the training from 103.51 to 0.108 s and testing time from 29.62 to 0.054 s on the UNSW-NB15 dataset. The proposed IDS model has been compared with other existing recent approaches and the proposed approach achieves the highest accuracy of 99.8% and lowest FAR of 0.02%.