An Efficient Fuzzy-Based Hybrid System to Cloud Intrusion Detection

Abstract

Cloud is a collection of resources such as hardware, networks, servers, storage, applications, and interfaces to provide on-demand services to customers. Since access to cloud is through internet, data stored in clouds are vulnerable to attacks from external as well as internal intruders. In order to preserve privacy of the data in cloud, several intrusion detection approaches, authentication techniques, and access control policies are being used. The common intrusion detection systems are predominantly incompetent to be used in cloud environments. In this paper, the usage of type-2 fuzzy neural network based on genetic algorithm is discussed to incorporate intrusion detection techniques into cloud. These systems are intelligent to gain knowledge of fuzzy sets and fuzzy rules from data to detect intrusions in a cloud environment. Using a standard benchmark data from a cloud intrusion detection dataset experiments are done, tested, and compared with other existing approaches in terms of detection rate accuracy, precision, recall, MSE, and scalability.