The Research Of Strategic Internet Intelligent Troubleshooting Strategy

The new generation strategic internet is a new type military network, constituted by physical layer, link transport layer, network control layer and application layer. It can flexibly support multi-service, and has anti-destroy, re-combined properties. With the development of computer science and communication network, the scale of strategic internet is growing larger, together with the emergence of more network applications. Owing to simple function, complex operation and lower efficiency, the old network troubleshooting system already can't satisfy for the demands of carrier development. In order to perform high efficiency and reliability, it is very important for us to set up a perfect network troubleshooting system. With the development of the present distributed network troubleshooting management, a self-adapting distributed network management framework based on dynamic SNMP agent groups is presented in this paper. The explanation of how to achieve this model is discussed. Especially, dynamic groups' management policy in the model is discussed, including grouping policy and choice of management mode, and a stable group Ω leader election algorithm based on loss links is given. According to the feature of strategic internet architecture, this paper brings layering-decentralization intelligence into this field, which makes it possible for the failure automatic location and diagnosis.The main achievements of this paper are as follows:(1) According to the immunology principles of bionics, a new physical layer nodefault location method-Immunology based Fault Location Method is presented. Inthis paper event detection sequences are viewed as analogous to peptide. According to the principle of positive selection in Immunology, the system builds up its event database. The behavior model whose frequency is higher will be analyzed and processed first. It improves the speed and effectiveness of intrusion detection. Fault Location is based on the event detection sequences correlation, graph theory and adjacency matrix are two methods to get the root failure sets. With the relationship of failures, this paper gives a method to determine the source of failure in this paper, which will perform failure filtration and location function effectively. The experiment system implemented by this method shows a good diagnostic ability.(2) Puts forward RSNN algorithm, a designing fault diagnosis method for link transport layer, which tightly combines neural network and rough sets. We can get reducedinformation table, which implies that the number of evaluation criteria is reduced with no information loss through rough set approach. And then, this reduced information is used to develop classification rules and train neural network to infer appropriate parameter. The rules developed by RS-Neural network analysis show the best prediction accuracy, if a case does match any of the rules. It's capable of overcoming several shortcomings in existing diagnosis systems, such as a dilemma between stability and redundancy. Since the essence of fault diagnosis is a kind of mapping, an artificial neural network model is adopted to deal with the mapping relation, categorizing the network faults. The experiment system implemented by this method shows a good diagnostic ability.(3) Consisting of weak T-norm cluster fuzzy neuron, a rough fuzzy neural network (RFNN) is constructed in this paper, which is applied to network control layercongestion inference. RFNN overcomes a few shortcoming of the conventional CRI, and it is much easier to satisfy consistency principle of fuzzy inference than CRI. Analyzed the properties of the new method, we discovered that it is continuous and monotonic. The reasoning results prove better performance obtained than other conventional congestion methods.(4) A framework of SVM based Network Fault Detection System of Application Layer is proposed. The function, mechanism and realization of the components of this framework are discussed in the paper. By means of distance metric of heterogeneous datasets, the feature data of network are preprocessed. Based on guaranteed estimators, we estimate the size of test set. Thus we not only avoid bad train result for lack of examples, but also reduce the training time and improve the efficiency of training. During the training, by means of fuzzy mathematics, considering the effect of different network data features to the classification, a weight method is brought forward. It improves the accuracy of network fault detection. The problem of low detection accuracy of some types of faults for the imbalance of training examples is researched. A method of increasing the proportion of the examples of these types is presented. It improves the detection accuracy of these types of faults.(5) A framework model proposed in this paper is a data-link redundant strategy based on reliability theory. The redundant running(RUS) could be combined with the normal maintenance, which greatly improves the performance of the network system. The static-checking and policy of authentication mechanism ensure the running network without any error. The redundant equipments are independent but are capable of communication with each other when they work their actions. The model is independent of...