Machine-learning-based daily access model implementation method and system

Abstract

The invention discloses a machine-learning-based daily access model implementation method and system. The method includes steps of A: setting a time range of traffic self learning; B: setting a networking terminal range of traffic self learning and a to-be-accessed service system list; C: collecting and analyzing traffic; D: forming a traffic analyzing result; E: creating an abnormity access rule of a traffic model; and F: generating the traffic model according to the analyzing result and the abnormity access rule and monitoring network access constantly through the traffic model. According to the invention, machine learning on actual traffic condition of an enterprise can be realized. Through a period of time of self learning, a daily access rule (which is a rule of access to an enterprise service system by a network terminal) meeting the practical condition of the enterprise can be obtained. A security manager only needs to make fine adjustment on a practical access rule according to a practical access control requirement of the enterprise, so that abnormal access precision of the enterprise can be improved. Besides, by using the daily access model provided by the invention, optimization or detection of safety equipment strategies can be performed.

Description

technical field [0001] The invention relates to the field of network monitoring, in particular to a machine learning-based daily access model realization method and system. Background technique [0002] Generally speaking, if the enterprise has a large number of internal assets, if the access control planning is not done well in the early stage, the control boundary will be blurred and management will be more difficult. Moreover, due to the uncertainty of the internal equipment assets of the enterprise, there is a certain frequency of changes (such as increase or decrease), which will increase the complexity of access control management. Ten network terminals, if the corresponding management strategy is not adjusted in time at this time, it will reduce the accuracy of access control management and increase the false alarm rate, and even lead to abnormal network access. [0003] Therefore, the prior art still needs to be improved and developed. Contents of the invention ...

AI Technical Summary

Problems solved by technology

[0004] In view of the above-mentioned deficiencies in the prior art, the purpose of the present invention is to provide a method and system for implementing a daily access model based on machine learning, aiming to solve the problems of the existing network access management methods with low accuracy and easy false positives leading to abnormal network access The problem

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