Neural networks applicated in computer network security evaluation and reliability analysis

Computer network in the information age is undoubtedly an important bearing basis, it has become an important guarantee for the healthy functioning of all walks of life, in this context of computer network security has been the concern of the community. Computer networks in the development process its effective safety evaluation has become an indispensable work, and neural network is an important and effective evaluation tool in the process of evolving neural network, which is also the application surface getting bigger. Article on neural network in computer network security evaluation were analyzed and put forward relevant views. This paper establishes the index system of evaluation of computer network security, Computer network security evaluation method based on BP neural network is designed to build the evaluation model by MATLAB simulation results show that the method is feasible.  With the rapid development of computer and network technology and extensive application of factors affecting more and more network security, network security issues become more complex, which requires the use of the system and network managers must be able to secure the network [1 3] The degree of effective measurement, network security have a clear understanding of the exact extent of network security control systems and the factors affecting safety in order to protect the safety of the system to the maximum extent. Important ways and means of network security evaluation is to solve this problem [4, 5] From a macro point of view the current computer has completely permeated society which, in all walks of life have an important role as a computer network environment running on the basis of environmental, safety issues and more and more attention [6] In fact many network security constraints, including not only the invading virus, but also affected its own vulnerabilities, and these constraints there is a close link between interrelated strong, reflecting the complex, non changeable linear relationship, which makes network security there have been many uncertainties, and to the smooth implementation of the relevant evaluation has brought a degree of retardation. The neural network is currently the most widely used one of a class of network security evaluation tools, BP neural network gradient descent algorithm as the basis, the characteristic is more significant, so the wide range of applications [7 9] Compared with traditional network neural network embodies the following characteristics: linear. Under normal circumstances the neural network needs to provide a periodic training process, after the completion of the training process can be more ideal efficiency. Fault tolerance. Compared with the traditional model in terms of the neural network is highly fault tolerant, and does not show irritation even when insufficient information integrity situation. The reason is mainly because of the presence of the above characteristics of neural network nodes are generally the local problem or feature among the nodes are identified and reflected the situation input node defects will not have integrated features of neural networks have an impact. Neural network environment has a good affinity with the match, reflecting a strong ability to adapt. In some cases the neural network is in the input mode or output 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016) (c) 2016. The authors Published by Atlantis Press 105 mode state, and by self training neural networks have a "memory" role, and thus lends itself to maintain stable operating state. The basic principle of neural network The basic structure of the nervous system neurons (nerve cells) it is the basic unit of processing of mutual transmission of information between the various parts of the body [10] According to the research results show that neural biologist, 1000 to 1011 neurons generally have the human brain. Each neuron consists of a cell body, a connecting to other neuronal axons projecting outward and some other shorter branches dendrites. The axon is the function of the output signal of the neurons is transferred to other neurons. Many nerve endings at the end of that excitement can simultaneously to multiple neurons. The function is received from other neurons. All the signals in the cell body of a neuron will receive the simply treated by axonal output. As shown in Figure 1 is the basis for most neural network model.