A method and system for detecting malware in an adversarial network
A malicious software and confrontational technology, applied in the field of network security, can solve problems such as poor detection stability, insufficient number of malicious software, and unbalanced number detection models, and achieve the effect of improving detection capabilities
Active Publication Date: 2021-09-03
武汉思普崚技术有限公司
View PDF6 Cites 0 Cited by
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
[0002] Although the existing statistical analysis and machine learning can detect malware, malicious code, malicious behavior, etc., there are still two shortcomings: first, the amount of malware in the training process is insufficient, which is far less than normal data. Imbalance will lead to the imbalance of the detection model, resulting in poor detection stability; second, with the development of technology, the attack methods of malware are constantly changing, but they cannot be used for model training in advance, resulting in the model being unable to detect unknown malicious software
Method used
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View moreImage
Smart Image Click on the blue labels to locate them in the text.
Smart ImageViewing Examples
Examples
Experimental program
Comparison scheme
Effect test
Embodiment Construction
[0037] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, so that the advantages and features of the present invention can be more easily understood by those skilled in the art, so as to define the protection scope of the present invention more clearly.
[0038] figure 1 A flow chart of the malware detection method of the adversarial network provided by this application, the method includes:
[0039] Obtain historical software data, analyze and extract feature vectors of malware in historical software data according to the characteristics of known malware types;
[0040] Input the known normal software and malicious software in the historical software into a black box model, and the black box model will mark the input normal software and malicious software to generate software samples;
[0041] Based on the eigenvectors of the malware, a noise simulation malware model is constructed, and the mode...
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More PUM
Login to View More Abstract
The invention provides an adversarial network malware detection method and system, which can analyze and construct a noise simulated malware model based on historical software data, input normal software and malware into a black box model, mark it, and generate software samples , using the software samples to train the noise simulation malware model, and the model itself has the ability to continuously compound and mutate malware. After the noise simulation malware model is trained, it is connected to the machine learning module as a simulated malware source for the machine learning module, and the malware trains the machine learning module continuously, helping to improve the detection ability of the machine learning module.
Description
technical field [0001] The present application relates to the technical field of network security, and in particular to a malware detection method and system for an adversarial network. Background technique [0002] Although the existing statistical analysis and machine learning can detect malware, malicious code, malicious behavior, etc., there are still two shortcomings: first, the amount of malware in the training process is insufficient, which is far less than normal data. Imbalance will lead to the imbalance of the detection model, resulting in poor detection stability; second, with the development of technology, the attack methods of malware are constantly changing, but they cannot be used for model training in advance, resulting in the model being unable to detect unknown malicious software. Therefore, there is an urgent need for a method and system that can self-generate usable malware, enhance training data, and improve the performance of detection models. Conten...
Claims
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More Application Information
Patent Timeline
Login to View More Patent Type & Authority Patents(China)
IPC IPC(8): G06F21/56G06N3/04G06N3/08G06N20/00
CPCG06F21/566G06N3/08G06N20/00G06F2221/033G06N3/045
Inventor 段彬
Owner 武汉思普崚技术有限公司