Network security assessment method and system based on machine learning

The invention discloses a network security assessment method and system based on machine learning, an XGBoost model is trained through multi-dimensional network security parameter historical data and corresponding security score labels, the trained XGBoost model can be directly used for network secu...

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Bibliographic Details
Main Authors LUO GUANGCHAO, LI JI, CHEN XIAOFENG, HU WEI, ZHAO YUANJIE, HAN BING, CHEN YOULEI, LI KE, LIANG LULU
Format Patent
LanguageChinese
English
Published 29.04.2022
Subjects
CALCULATING
COMPUTING
COUNTING
ELECTRIC COMMUNICATION TECHNIQUE
ELECTRICITY
HANDLING RECORD CARRIERS
PHYSICS
PRESENTATION OF DATA
RECOGNITION OF DATA
RECORD CARRIERS
TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHICCOMMUNICATION
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Summary:The invention discloses a network security assessment method and system based on machine learning, an XGBoost model is trained through multi-dimensional network security parameter historical data and corresponding security score labels, the trained XGBoost model can be directly used for network security scoring, the network security assessment does not need to depend on experts any more, and the network security assessment efficiency is improved. The technical problems that in the prior art, an expert scoring method is adopted for network security evaluation, dependence on expert experience is achieved, subjectivity is high, time cost is high, efficiency is low, reliability is not high, and the requirement for network security evaluation in the big data environment is difficult to meet are solved. The security score label is formulated through evaluation of a plurality of experts, a data label used for training an XGBoost model is finally obtained, then a model used for network security evaluation is obtained
Bibliography:Application Number: CN202210308554