Behavioral Analysis Approach for Likelihood Determination in Cloud IDS: Implementation Architecture

Abstract

Cyberattacks present a ubiquitous threat to an enterprise's information in Cloud Computing assets and must be properly controlled. The use of the current generation of IDS have various limitations on their performance making them not effective for cloud computing security and could generate a huge number of false-positive alarms. Analyzing intrusion based on attack patterns and risk assessment has demonstrated its efficiency in reducing the number of false alarms and optimizing the IDS performances. However, the use of the same value of likelihood makes the approach lack a real risk value determination. This article intends to present a new probabilistic and behavioral approach for likelihood determination to quantify attacks in cloud environment. With the main task to increase the efficiency of IDS and decrease the number of alarms. Experimental results show that the approach is superior to the state-of-the-art approach for intrusion detection in the cloud.