Periodic Service Behavior Strain Analysis-Based Intrusion Detection in Cloud

Abstract

The problem of intrusion detection in cloud environments has been well studied. The presence of adversaries would challenge data security in the cloud by generating intrusion attacks towards the cloud data and should be mitigated for the development of the cloud environment. In mitigating intrusion attacks, there exist several techniques in the literature. The method uses different features like frequency of access, payload details, protocol mapping, etc. However, the methods need to improve to achieve the expected performance in detecting intrusion attacks. An efficient Periodic Service Behavior Strain Analysis (PSBSA) is presented to handle this issue. Unlike earlier methods, the PSBSA model analyzes the behavior of users in various time frames like historical, recent, and current spans. The model focused on identifying intrusion attacks in several constraints, not just considering the current nature. The performance of intrusion detection can be improved by viewing the user's behavior in historical, present, and recent timespan. Unlike other approaches, the proposed PSBSA model considers the user's behavior at different times in measuring the user's trust towards intrusion detection. Accordingly, the proposed PSBSA model analyzes the behavior of users under various situations. It examines the behavior in accessing the services at historical, current, and recent times. The method performs Historical Strain Analysis (HSA) Current Strain Analysis (CSA) and Recent Strain Analysis (RSA). HSA analysis is performed according to the historical data, CSA is performed based on the current access data and RSA is performed with the recent access data. The model estimates various legitimacy support values on each analysis to conclude the trust of any user. According to the support values, intrusion detection has been performed. The proposed PSBSA model introduces higher accuracy in intrusion detection in a cloud environment.