Performance Comparison of Neuro-Fuzzy Cloud Intrusion Detection Systems

Sivakami Raja1 and Saravanan Ramaiah2

1Department of Information Technology, PSNA College of Engineering and Technology, India

2Department of Computer Science and Engineering, RVS Educational Trust's Group of Institutions, India

Abstract: Cloud computing is a subscription-based service where we can obtain networked storage space and computer resources. 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 techniques, authentication methods and access control policies are being used. The common intrusion detection systems are predominantly incompetent to be deployed in cloud environments due to their openness and specific essence. In this paper, we compare soft computing approaches based on type-1, type-2 and interval type-2 fuzzy-neural systems to detect intrusions in a cloud environment. Using a standard benchmark data from a Cloud Intrusion Detection Dataset (CIDD) derived from DARPA Intrusion Detection Evaluation Group of MIT Lincoln Laboratory, experiments are conducted and the results are presented in terms of mean square error.

Keywords: Fuzzy neural networks, hybrid intelligent systems, intrusion detection, partitioning algorithms, pattern analysis.

Received September 13, 2015; accepted October 18, 2015