Attack characteristic detection for large scale network based on collaborative planning

Abstract

In the process of research on the characteristic detection method for large scale network attacks, due to the use of the current algorithm for large-scale network attack detection, it is unable to describe the attack characteristics of network attacks or detect in low accuracy. Therefore, an attack detection method for large scale network based on cooperative planning is proposed. The method is based on collaborative planning to design the detection characteristic of large scale network attacks, which is transformed into a space search problem. The difference between the parameters of the flow vector and the normal vector of the normal network spatial data is extracted as characteristics, combining Gauss mixture model with the expectation maximization algorithm, to design Lorenz chaotic asynchronous tracking detection algorithm for modeling and detection of network data stream. The experimental simulation shows that the detection method of large scale network attack detection method has high accuracy and high efficiency. Currently, characteristic detection technology for network attack is based on ant colony algorithm, particle algorithm and the fuzzy algorithm for large-scale network. Among them, the commonly characteristic detection method is technology based on the fuzzy algorithm for large scale network attack. However the current algorithms with characteristic detection for network attack could not describe attack characteristics in detail, which lead to low accuracy. In view of the above defect, a method for large scale network attack detection based on cooperative planning is proposed. The method is represented for network attack with characteristic feature, which is transformed into a search problem in the space. To extract difference of parameter vector characteristic of network spatial data flow between to test network and normal network, with Gaussian mixture model and expectation maximization algorithm combined, Lorenz chaotic asynchronous detecting and tracking algorithm is designed to model and test the network data flow. The experimental simulation shows that the detection method for large scale network has high accuracy and efficiency.