Malicious software detection method and device

A technology of malware and detection methods, applied in neural learning methods, computer security devices, computer parts, etc., to avoid human influence and improve detection efficiency and accuracy

Pending Publication Date: 2021-09-21
HANGZHOU DPTECH TECH
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  • Claims
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Therefore, it is a good choice to make some attempts to replace artificial intelligence with artificial intelli

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  • Malicious software detection method and device
  • Malicious software detection method and device
  • Malicious software detection method and device

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Example Embodiment

[0032] An example embodiment will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in a variety of forms, and is not to be construed as being limited to the embodiments set forth herein; in contrast, the present disclosure will be fully and complete, and the concept of example embodiments is fully communicated A technician in the art. The same reference numerals are denoted by the same reference numerals, therefore will omit their repeated description thereof.

[0033] Furthermore, the features, structures, or characteristics described may be incorporated in one or more embodiments in any suitable manner. In the following description, there is provided a number of specific details to give a sufficient understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that there may be one or more of the present disclosure without one or more of a particular detail...

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Abstract

The invention relates to a malicious software detection method and device, electronic equipment and a computer readable medium. The method comprises the following steps: obtaining target software to be detected; converting the target software into at least one grayscale image; inputting the at least one gray level image into a malicious software recognition model, generating at least one malicious software probability, wherein the malicious software recognition model is generated through training of a convolutional neural network model; when the at least one malicious software probability meets a preset strategy, determining that the target software is malicious software. According to the malicious software detection method and device, the electronic equipment and the computer readable medium, the knowledge reserve requirement for personnel during malicious software detection can be reduced, human influence is avoided, malicious software is automatically detected, and the detection efficiency and accuracy are improved.

Description

technical field [0001] The present disclosure relates to the field of computer information processing, in particular, to a malware detection method, device, electronic equipment, and computer-readable medium. Background technique [0002] Today, with the rapid application and popularization of artificial intelligence, most of the accurate judgment of whether a new software is malware still requires manual reverse engineering. Traditional reverse engineering is mainly divided into static and dynamic. Static analysis mainly uses tools to collect software information, research software structure and sensitive strings. Dynamic analysis mainly involves running the software under test using a virtual environment to infer or confirm the behavior of the software under test through observation, tracing. In an environment where learning programming is advocated and more and more people know how to program, new software emerges in an endless stream. At the same time, with the high p...

Claims

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Application Information

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IPC IPC(8): G06F21/56G06K9/62G06N3/04G06N3/08
CPCG06F21/562G06N3/08G06N3/045G06F18/2414G06F18/2415
Inventor 曹晨煜
Owner HANGZHOU DPTECH TECH
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