IJMIE Volume 3, Issue 9 ISSN: 2249-0558 __________________________________________________________ A Monthly Double-Blind Peer Reviewed Refereed Open Access International e-Journal - Included in the International Serial Directories Indexed & Listed at: Ulrich's Periodicals Directory ©, U.S.A., Open J-Gage as well as in Cabell’s Directories of Publishing Opportunities, U.S.A. International Journal of Management, IT and Engineering http://www.ijmra.us 197 September 2013 Secure Network ID and Attack Measure Selection in Virtual Network S.Uvaraj  S.Suresh  Abstract Cloud security is one of most important issues that has 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). DDoS attacks usually involve early stage actions such as multi-step exploitation, low frequency vulnerability scanning, and compromising identified vulnerable virtual machines as zombies, and finally DDoS attacks through the compromised zombies. 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, 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. The system and security evaluations demonstrate the efficiency and effectiveness of the proposed solution. Keywords - Network Security, Cloud Computing, Intrusion Detection, Attack Graph, Zombie Detection  M.E/CSE, Arulmigu Meenakshi Amman College of Engineering, Kanchipuram, India  B.Tech/IT, Sri Venkateswara College of Engineering, Chennai, India