Visual classification method and device for Internet of Things malicious software and electronic device

Abstract

The embodiment of the invention provides a visual classification method and device for Internet of Things malicious software and an electronic device, and the method comprises the steps: carrying outthe binary file extraction of to-be-classified Internet of Things malicious software, obtaining a binary malicious code, and carrying out the bit combination of the binary malicious code to generate agray-scale image; and performing feature extraction on the grayscale image, and based on the extracted grayscale image features, utilizing a deep neural network classification model based on a 2-bitnetwork to obtain a classification category to which the to-be-classified Internet of Things malicious software belongs. According to the embodiment of the invention, a classification model based on a2-bit network is adopted, model size and calculation time can be substantially reduced, the processed malicious software is converted into the gray level image, feature extraction is carried out according to the gray level image, visual features of extracted features can be improved, the classification process is made to have higher interpretability, and classification of the Internet of Things malicious software can be more reliably and efficiently accomplished.

Description

technical field [0001] The present invention relates to the technical field of malicious software classification, and more specifically, to a method, device and electronic equipment for visual classification of Internet of Things malicious software. Background technique [0002] The Internet of Things is an extension of the traditional network as its core, and it is a network that enables all ordinary objects that can perform independent functions to realize interconnection. IoT devices are different from traditional sensors, they have more powerful computing power, and can intelligently solve some complex problems. However, the complexity of hardware and software leads to many vulnerabilities in IoT devices, which also gives attackers more opportunities to attack IoT devices through malware. [0003] Malicious software is also commonly referred to as rogue software, which refers to software that is installed and run on the user's computer or other terminal without explicit...

AI Technical Summary

Problems solved by technology

[0005] However, although the method based on deep learning can improve the efficiency of malware classification work to a certain extent, the features extracted by this method are difficult to explain, and the reliability and importance of the extracted features cannot be proved.

Moreover, due to the lack of sufficient computing resources in IoT devices, this deep learning method that needs to store and process a large number of parameters is still difficult to meet the needs of malware classification speed.

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