HTTP traffic feature recognition and extraction method based on machine learning

Abstract

The invention discloses an HTTP (Hyper Text Transport Protocol) traffic feature recognition and extraction method based on machine learning. The method comprises the following steps: step 1, carryingout HTTP traffic recognition and acquisition; step 2, carrying out feature detection and generating rules and step 3, extracting HTTP traffic characteristics. Compared with feature extraction based onregular expressions on the existing market, the method has the advantages that the feature accuracy is improved, the probability of mistakenly extracting dirty data by the regular expressions is reduced, and compared with a feature marking method based on manpower, the labor cost input and the response feedback time to novel features are reduced. Meanwhile, in the patent, feature / rule generationand feature extraction are separated, a unique extraction engine can be designed, and the feature extraction efficiency is improved.

Description

technical field [0001] The invention relates to a method for identifying and extracting HTTP traffic features based on machine learning. Background technique [0002] In the Internet society, there is a large amount of HTTP traffic on the network, and there is a large amount of valuable data in the HTTP traffic. Collecting these data and integrating them into a knowledge base helps to understand information in a timely manner, respond to events, and make decisions. At present, there are many methods for parsing HTTP data and extracting effective features, such as extraction based on regular expressions, extraction based on feature matching, and methods based on machine learning to identify features. [0003] Although some feature recognition and extraction products have been launched on the market, these products have certain shortcomings. Extraction based on feature formats such as regular expressions and state machines is easy to extract features by mistake, pollute the fe...

AI Technical Summary

Problems solved by technology

[0003] Although some feature recognition and extraction products have been launched on the market, these products have certain shortcomings. Extraction based on feature formats such as regular expressions and state machines is easy to extract features by mistake, pollute the feature library, and make the entire feature library unusable. Feature matching extraction based on manual analysis requires a large labor cost and does not respond to new features in a timely manner. The method based on machine learning is a great test for extraction efficiency and cannot cope with the impact of large-flow data.

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