Leveraging feature subset selection with deer hunting optimizer based deep learning for anomaly detection in secure cloud environment.

Cloud computing (CC) was extremely implemented by the application service providers (ASP) and enterprises for reducing either operational or capital expenditures. Services and applications before running on remote data centres can presently be transferred to public or private clouds. This alteration can manage the study community to analyze cloud platforms. But, a wide acceptance of such platforms can be blocked by main security concerns. Firewalls and typical rule-based security protection approaches could not be adequate for protecting user information in cloud environments. In recent times, advances in machine learning (ML) approaches are concerned the attention of research communities for building intrusion detection systems (IDS) that anomaly detection from the network traffic. This manuscript presents a Feature Subset Selection with Deer Hunting Optimizer based Deep Learning (FSS-DHODLAD) technique for Anomaly Detection in Secure Cloud Computing Environment. The purpose of the FSS-DHODLAD algorithm lies in the accurate identification and classification of anomalies that exist in the cloud platform. To reduce the high dimensionality of features, the FSS-DHODLAD technique designs a grasshopper optimization algorithm (GOA) to elect features. For anomaly detection, the FSS-DHODLAD technique utilizes Attention Convolutional Bidirectional Long Short Term Memory (AC-BLSTM) system. Eventually, the DHO system was utilized for the optimal hyperparameter selection of the AC-BLSTM model. To exhibit the higher performance of the FSS-DHODLAD system, a sequence of simulations was applied to the benchmark database and the outcomes are assessed concerning several evaluation measures. The simulation outcomes implied the greater performance of the FSS-DHODLAD algorithm over other recent approaches in terms of different metrics. [ABSTRACT FROM AUTHOR]