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A Malicious Code Variants Detection Method Based on Self-attention

Published: 29 May 2020 Publication History

Abstract

Due to the emergence of variants and polymorphic technologies, the number of malicious codes that attack smart devices is growing rapidly. However, few of these viruses are new types of malicious code, and the remaining large numbers are variants of existing malicious code. Therefore, the detection of malicious code variants is necessary. The detection accuracy and efficiency of existing malicious code variant detection methods cannot perform satisfactory results. Therefore, this paper proposed a new detection method based on deep learning for detecting malicious code variants. First, we converted the malicious code into a visual grayscale image and then built a convolutional neural network including self-attention mechanism, which was set before the the convolutional neural network. The generated malicious code images will be input to the convolutional neural network for automatic identification and classification. In order to test our method, we conducted a series of experiments based on the Malimg dataset, and the results showed that our model has higher accuracy and faster speed than other methods.

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Cited By

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  • (2023)A key code detection model based on semantic convolutional memory fusion networkFourth International Conference on Signal Processing and Computer Science (SPCS 2023)10.1117/12.3012088(12)Online publication date: 21-Dec-2023
  • (2022)Malware Family Prediction with an Awareness of Label UncertaintyThe Computer Journal10.1093/comjnl/bxac181Online publication date: 17-Dec-2022
  • (2021)MalCaps: A Capsule Network Based Model for the Malware ClassificationProcesses10.3390/pr90609299:6(929)Online publication date: 25-May-2021

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  1. A Malicious Code Variants Detection Method Based on Self-attention

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    cover image ACM Other conferences
    ICCTA '20: Proceedings of the 2020 6th International Conference on Computer and Technology Applications
    April 2020
    178 pages
    ISBN:9781450377492
    DOI:10.1145/3397125
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 29 May 2020

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    Author Tags

    1. CNN
    2. Malicious code variant detection
    3. overlap
    4. self-attention

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    View all
    • (2023)A key code detection model based on semantic convolutional memory fusion networkFourth International Conference on Signal Processing and Computer Science (SPCS 2023)10.1117/12.3012088(12)Online publication date: 21-Dec-2023
    • (2022)Malware Family Prediction with an Awareness of Label UncertaintyThe Computer Journal10.1093/comjnl/bxac181Online publication date: 17-Dec-2022
    • (2021)MalCaps: A Capsule Network Based Model for the Malware ClassificationProcesses10.3390/pr90609299:6(929)Online publication date: 25-May-2021

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