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      HIT4Mal: Hybrid image transformation for malware classification

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          Abstract

          Modern malware evolves various detection avoidance techniques to bypass the state‐of‐the‐art detection methods. An emerging trend to deal with this issue is the combination of image transformation and machine learning models to classify and detect malware. However, existing works in this field only perform simple image transformation methods. These simple transformations have not considered color encoding and pixel rendering techniques on the performance of machine learning classifiers. In this article, we propose a novel approach to encoding and arranging bytes from binary files into images. These developed images contain statistical (eg, entropy) and syntactic artifacts (eg, strings), and their pixels are filled up using space‐filling curves. Thanks to these features, our encoding method surpasses existing methods demonstrated by extensive experiments. In particular, our proposed method achieved 93.01% accuracy using the combination of the entropy encoding and character class scheme on the Hilbert curve.

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          Most cited references47

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          Deep Residual Learning for Image Recognition

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            A Mathematical Theory of Communication

            C. Shannon (1948)
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              Gradient-based learning applied to document recognition

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                Author and article information

                Contributors
                Journal
                Transactions on Emerging Telecommunications Technologies
                Trans Emerging Tel Tech
                Wiley
                2161-3915
                2161-3915
                November 2020
                November 20 2019
                November 2020
                : 31
                : 11
                Affiliations
                [1 ] Department of Information Engineering and Computer Science University of Trento Trento Italy
                [2 ] Department of Computer Science Hongik University Seoul South Korea
                [3 ] Department of Computer Science University of Dayton Dayton Ohio
                [4 ] Department of Computer Science Purdue University Fort Wayne Fort Wayne Indiana
                Article
                10.1002/ett.3789
                3b706292-ce86-433b-b319-dfd4d3bd3427
                © 2020

                http://onlinelibrary.wiley.com/termsAndConditions#vor

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