Metaheuristic Algorithms for Optimization and Feature Selection in Cloud Data Classification Using Convolutional Neural Network

Cloud Computing has drastically simplified the management of IT resources by introducing the concept of resource pooling. It has led to a tremendous improvement in infrastructure planning. The major goals of cloud computing include maximization of computing resources with minimization of cost. But the truth is that everything has a price and cloud computing is no different. With Cloud computing there comes a number of security concerns which need to be addressed. Cloud forensics plays a vital role to address the security issues related to cloud computing by identifying, collecting and studying digital evidence in cloud environment.The aim of the research paper is to explore the concept of cloud forensic by applying optimization for feature selection before classification of data on cloud side. The data is classified as malicious and non-malicious using convolutional neural network. The proposed system makes a comparison of models with and without feature selection algorithms before applying the data to CNN. A comparison of different metaheuristics algorithms- Particle Swarm Optimization, Shuffled Frog Leap Optimization and Fire fly algorithm for feature optimization is done based on convergence rate and efficiency.