Dynamic Service Orient Target Analysis Model for Improved Intrusion Detection in Cloud

Abstract

Dynamic Service Orient Target Analysis Model for Improved Intrusion Detection in Cloud 

The problem of cloud security has been well studied and there exist several approaches to improve the security in cloud environment. They suffer with poor security performance which challenges the QoS achievement. To handle this issue, an efficient Dynamic Service orient Target Analysis Model (DSTAM) is presented in this article. Instead of identifying the intrusion attack in data level, the proposed approach identifies the presence of threat at service level. The service level threat is analyzed by adapting DSTAM, which performs Frequency Analysis according to the frequency of service access, performs QoS support Analysis according to different factors like throughput, completion and so on. Also, the method analyzes the Service Availability based on the hanging features. According to the result of frequency analysis, Qos Support Analysis, and Availability Analysis, the method estimates the trust score for the different services. According to the value of Trust Score, the method performs intrusion detection to improve the QoS of the environment.