Anomaly secure detection methods by analyzing dynamic characteristics of the network traffic in cloud communications

Cloud computing represents a new paradigm where computing resources are offered as services in the world via communication Internet. As many new types of attacks are arising at a high frequency, the cloud computing services are exposed to an increasing amount of security threats. To reduce security risks, two approaches of the network traffic anomaly detection in cloud communications have been presented, which analyze dynamic characteristics of the network traffic based on the synergetic neural networks and the catastrophe theory. In the former approach, a synergetic dynamic equation with a group of the order parameters is used to describe the complex behaviors of the network traffic system in cloud communications. When this equation is evolved, only the order parameter determined by the primary factors can converge to 1. Then, the anomaly can be detected. In the latter approach, a catastrophe potential function is introduced to describe the catastrophe dynamic process of the network traffic in cloud communications. When anomalies occur, the state of the network traffic will deviate from the normal one. To assess the deviation, an index named as catastrophe distance is defined. The network traffic anomaly can be detected by the value of this index. We evaluate the performance of these two approaches using the standard Defense Advanced Research Projects Agency data sets. Experimental results show that our approaches can effectively detect the network traffic anomaly and achieve the high detection probability and the low false alarms rate.