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Malicious software detection method and device

A malware and detection method technology, applied in the field of network security, can solve the problems of high false positive rate, unknown size of the software to be detected, and unfixed

Active Publication Date: 2021-02-09
BEIJING TOPSEC NETWORK SECURITY TECH +2
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, since the size of the software to be detected is unknown and not fixed, interference and noise values ​​will be introduced in the process of ma

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  • Malicious software detection method and device

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Embodiment Construction

[0076] In order to understand the above-mentioned purpose, features and advantages of the present invention more clearly, the solutions of the present invention will be further described below. It should be noted that, in the case of no conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other.

[0077] In the following description, many specific details have been set forth in order to fully understand the present invention, but the present invention can also be implemented in other ways different from those described here; obviously, the embodiments in the description are only some embodiments of the present invention, and Not all examples.

[0078] The term "and / or" in this article is just an association relationship describing associated objects, which means that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist simultaneously, and there exists alone B these three s...

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Abstract

The embodiment of the invention provides a malicious software detection method and device, and relates to the technical field of network security. The method comprises the steps that to-be-detected software is converted into a first image and a second image, the row width and the column width of the first image are both N, and the row width and the column width of the second image are both M; averagely segmenting the first image into L third images; predicting the probability that each image of the model is each software category based on deep learning; whether the hit number is larger than Xor not is judged, the hit number is the number of the images hit the target software category in the to-be-predicted image set, and the software category hit by any image in the to-be-predicted imageset is the software category corresponding to the maximum value in the probability of the image; wherein the target software category is the software category with the most hit times of the image in the to-be-predicted image set; and if not, outputting indication information for indicating that the software is the non-malicious software. The embodiment of the invention is used for reducing the false alarm rate during malicious software detection.

Description

technical field [0001] The invention relates to the technical field of network security, in particular to a malicious software detection method and device. Background technique [0002] Malicious software refers to software that is installed and run on the user's computer or other terminals without explicitly prompting the user or without the user's permission, and infringes the user's legitimate rights and interests. Due to the growing number of malware and its variants, malware detection techniques are gaining more and more attention. [0003] The traditional malware detection method is a static detection method, which mainly includes the usual feature matching detection and heuristic detection. With the continuous growth of the number of malware and its variants, traditional static detection methods cannot meet the detection rate and classification requirements of malware detection. In order to further improve the detection rate of malware detection, the existing technol...

Claims

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Application Information

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IPC IPC(8): G06F21/56G06T7/10
CPCG06F21/56G06T2207/20081G06T2207/20084G06T7/10
Inventor 李海燕庞瑞
Owner BEIJING TOPSEC NETWORK SECURITY TECH