The work proposes and investigates an intelligent method and algorithms for on-line assessment of the state of network elements to ensure the required quality indicators of provided communication services in corporate high-speed multiservice communication networks. The developed method and algorithms operate in a mode close to real time. One of the features of corporate multiservice communication networks is the high dynamics of changes in their state. The main task of the automated control system, which is an integral part of the corporate multiservice communication network, is to ensure the specified quality of the provided communication services to the consumer. Thus, the relevance of the research presented in the work is due to the fact that most of the management processes in corporate high-speed multiservice communication networks must be implemented in a mode close to real time with a given quality. The basis of the method for operational assessment of the state of network elements is the concept of creating and using intelligent agents. In the proposed approach, intelligent agents are created as hierarchical fuzzy situational networks, in which control solutions, in contrast to known methods based on the use of reference situations, are applied based on solving a hierarchical set of optimization problems using fuzzy mathematical programming methods. The main paradigm of their functioning is “situation -action”.
In the paper, an adaptive hybrid heuristic (behavioral) method for detecting small traffic anomalies in high-speed multiservice communication networks, which operates in real time, is proposed and investigated. The relevance of this study is determined by the fact that network security management processes in high-speed multiservice communication networks need to be implemented in a mode close to real-time mode, as well as identifying possible network security threats in the early stages of the implementation of possible network attacks. The proposed method and algorithm belong to the class of adaptive methods and algorithms with preliminary training. The average relative error in estimating the evaluated traffic parameters does not exceed 10%, which is sufficient for the implementation of operational network management tasks. Anomalies of the expectation of traffic intensity and its dispersion are identified if their valuesexceed the normal values by 15% or more, which makes it possible to detect possible network attacks in the early phases of their implementation, for example, at the stage of scanning ports and interfaces of the attacked system. The procedure for detecting anomalous traffic behavior is implemented based on the Mamdani’s method of hierarchical fuzzy logical inference. A study of the proposed method for detecting anomalous behavior of network traffic showed its high efficiency.
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