Deep message parsing system based on self-learning

Abstract

The invention discloses a deep message parsing system based on self-learning, which belongs to the technical field of data processing and comprises a generation end and an operation end, the generation end comprises a first acquisition module, a first parsing module and a first learning module, and the first learning module specifically comprises a training unit. According to the technical scheme,the beneficial effects are as follows: through increasing learning centers, the data in the existing network environment can be subjected to machine learning; a learning result is changed into a feature model to be fed back to an existing feature library; the feature library has a self-updating capability and can be synchronized with the updating speed of the application program. The problem thatin the piror art, the feature library maintenance amount is large, and the consumed manpower and resources are large are solved, the existing feature library is continuously self-optimized by the system through the learning, feedback and re-learning processes, and the feature identification accuracy is greatly improved.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a deep message analysis system based on self-learning. Background technique [0002] DPI (Deep Packet Analysis) is a deep detection technology based on data packets. It performs deep detection for different network application layer loads, and determines its legality by detecting the payload of the message. The DPI deep message analysis system is now Widely used in the network environment, the system is mainly used in technical fields such as identifying application programs, identifying virtual identities, identifying network content, and monitoring network traffic. In the current Internet environment, there are many kinds of mobile phone and computer applications, which emerge in endlessly and are updated quickly. The update of the signature database cannot be kept in sync. The amount of data generated by network communication between various applications is huge. In-de...

AI Technical Summary

Problems solved by technology

[0003] According to the above-mentioned problems in the prior art, a deep message analysis system with self-learning function is provided. By adding a learning center, it is possible to perform machine learning on the data in the existing network environment, and turn the learning results into feature models. Feedback to the existing feature library, so that the feature library has the ability to update itself, and can keep pace with the update speed of the application program, which solves the problem of large maintenance of the feature library in the prior art, and consumes a lot of manpower and resources , at the same time, the system continuously self-optimizes the existing feature library through the process of learning, feedback, and re-learning, thus greatly improving the accuracy of feature recognition

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