A Hybrid Model is Proposed Based in The Combination of Genetic and MAFS in Cloud Environment

Abstract

Cloud computing is being heralded as an important trend in information technology throughout the world. Data security has a major issue in cloud computing environment; An intrusion detection system (IDS) is a component that helps to detect various types of malicious network traffic which cannot be detected by a conventional firewall. Many IDS have been developed based on machine learning techniques. In recent growth, advanced detection approaches created by combining or integrating multiple learning techniques have shown better detection performance than general single learning technique. The feature representation method is an important pattern classifier that facilitates correct classifications, however, there have been very few related studies focusing how to extractor representative features for normal connections and effective detection of attacks. The objective of this paper is to suggest new security mechanisms using various trust approaches in broker based federated cloud architecture, ranking the providers with the help of regression tree approach using Service Measurement Index security attributes and new hybrid computation intelligence built on the combination of genetic with Artificial Fish Swarm in Intrusion Detection system.