This paper proposes a model for detecting anomalous behavior in cloud computing. The model uses Software-Defined Networks to redirect virtual machine (VM) traffic so it can be captured and analyzed. The goal is to detect both known and unknown anomalous network behaviors between VMs. The model adopts hybrid techniques to analyze VM network behaviors and control network systems. Experimental results showed the approach was over 90% effective, proving the feasibility of the proposed anomalous behavior detection model.

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An Anomalous Behavior Detection Model in Cloud Computing
Abstract :
This paper proposes an anomalous behavior detection model based on cloud computing.
Virtual Machines (VMs) are one of the key components of cloud Infrastructure as a
Service (IaaS). The security of such VMs is critical to IaaS security. Many studies have
been done on cloud computing security issues, but research into VM security issues,
especially regarding VM network traffic anomalous behavior detection, remains
inadequate. More and more studies show that communication among internal nodes
exhibits complex patterns. Communication among VMs in cloud computing is invisible.
Researchers find such issues challenging, and few solutions have been
proposed???leaving cloud computing vulnerable to network attacks. This paper proposes
a model that uses Software-Defined Networks (SDN) to implement traffic redirection. Our
model can capture inter-VM traffic, detect known and unknown anomalous network
behaviors, adopt hybrid techniques to analyze VM network behaviors, and control
network systems. The experimental results indicate that the effectiveness of our approach
is greater than 90%, and prove the feasibility of the model.