NICE-D: A Modified Approach for Cloud Security

Cloud security is one of most important issues that have attracted a lot of research and development effort in past few years. Particularly, attackers can explore vulnerabilities of a cloud system and compromise virtual machines to deploy further large-scale Distributed Denial-of-Service (DDoS). Within the cloud system, especially the Infrastructure-as-a-Service (IaaS) clouds, the detection of zombie exploration attacks is extremely difficult. This is because cloud users may install vulnerable applications on their virtual machines. To prevent vulnerable virtual machines from being compromised in the cloud, we propose a multi-phase distributed vulnerability detection, measurement, and countermeasure selection mechanism called NICE [1], which is built on attack graph based analytical models and reconfigurable virtual network-based countermeasures. The proposed framework leverages Open Flow network programming APIs to build a monitor and control plane over distributed programmable virtual switches in order to significantly improve attack detection and mitigate attack consequences. Existing NICE model uses signature based IDS i. e. SNORT [1], In this project work we improved the intrusion detection accuracy of NICE by using dynamic intrusion detection system (NICE-D). Dynamic IDS monitors the incoming traffic flow and anomalous time slot, and accordingly generates the new signature to identify the future intrusions over cloud system. The system and security evaluations demonstrate the efficiency and effectiveness of the proposed solution.