Machine learning method and system based on context awareness
A machine learning and situational awareness technology, applied in the field of information security, can solve the problem of high false positive rate in detecting unknown threats, achieve the effect of reducing the false positive rate of detecting unknown threats and improving the detection rate
Active Publication Date: 2018-11-30
亚信科技(成都)有限公司
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[0005] The present invention provides a machine learning method and system based on situational awareness, which is used to solve the problem of high false alarm rate in detecting unknown threats using machine learning models in the prior art
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[0048] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of this application. The use of the terms "first" and "second" etc. does not denote any order, and the above terms may be interpreted as names of the described objects. In the embodiments of the present application, words such as "exemplary" or "for example" are used as examples, illustrations or illustrations. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of the present application shall not be interpreted as being more preferred or more advantageo...
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The embodiment of the invention provides a machine learning method and system based on context awareness, relates to the technical field of information safety and aims to solve the problem that in theprior art, the unknown threat false alarm rate through detection of a machine learning model is higher. The method comprises the steps that file statistic information of files is acquired, and to-be-detected files for machine learning detection are determined according to the file statistic information, wherein the files include, at least, static files and process files; context information of the to-be-detected files and machine learning model file characteristics for machine learning detection of the to-be-detected files are acquired; according to the context information, machine learning models of the to-be-detected files are determined, and the machine learning model file characteristics are input into the machine learning models to detect whether the to-be-detected files have unknownthreats or not. By adopting the method, the unknown threat false alarm rate through detection of a machine learning model can be reduced, and the unknown threat detection rate is improved.
Description
technical field [0001] The present invention relates to the technical field of information security, in particular to a machine learning method and system based on situation awareness. Background technique [0002] With the rapid development of digital technology, network security threats are also growing rapidly, especially more and more viruses use 0-day loopholes (has been discovered and undisclosed loopholes) to spread rapidly, making the speed and quantity of virus mutations as fast as How to deal with unknown threats among network security threats is facing severe challenges. [0003] Currently, virus detection for network security threats can be implemented based on signature matching or machine learning model detection. In practice, virus detection based on signature matching extracts signatures from virus samples and detects known viruses through signature matching. Although it has a very low false positive rate, it cannot cope with unknown viruses that spread thro...
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Login to View More IPC IPC(8): G06F21/56G06N99/00
CPCG06F21/56
Inventor母志武徐业礼梁宇
Owner亚信科技(成都)有限公司