Assessing IP Weight Metrics for Cloud Intrusion Detection using Machine Learning Techniques

Abstract

Despite the growing popularity of cloud computing, security is still an important concern of cloud customers and potential adopters. Cloud computing is prone to the same attack vectors as traditional networks, in addition to new attack vectors that are specific to cloud platforms. Intrusion Detection Systems (IDS) deployed in the cloud must take into account the specificity of the underlying threat landscape as well as the architectural and operational constraints of cloud platforms. In this project, an IDS that utilizes IP weight metrics for feature selection is implemented. Additionally, this system is tested with different supervised classification models and evaluated on a cloud intrusion dataset. In comparison with the results under conventional network environment, we conclude that the performance of IDS against cloud intrusions is promising, however, other developments such as unsupervised intrusion detection techniques and extra data preprocessing stages should be researched for the best practice of the system.