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Your search keyword '"Nguyen, Trong-Kha"' showing total 11 results
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11 results on '"Nguyen, Trong-Kha"'

1. A Convolutional Transformation Network for Malware Classification

Author

Vu, Duc-Ly, Nguyen, Trong-Kha, Nguyen, Tam V., Nguyen, Tu N., Massacci, Fabio, and Phung, Phu H.

Subjects
Computer Science - Cryptography and Security, Computer Science - Machine Learning
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 techniques to classify and detect malware. However, existing works in this field only perform simple image transformation methods that limit the accuracy of the detection. In this paper, we introduce a novel approach to classify malware by using a deep network on images transformed from binary samples. In particular, we first develop a novel hybrid image transformation method to convert binaries into color images that convey the binary semantics. The images are trained by a deep convolutional neural network that later classifies the test inputs into benign or malicious categories. Through the extensive experiments, our proposed method surpasses all baselines and achieves 99.14% in terms of accuracy on the testing set., Comment: 6 pages, 4 figures

Published
2019

2. An Efficient Searchable Encryption Scheme in the Multi-user Environment

Author

Kim, In Tae, Quan, Tran Hai, Duc, Ly Vu, Nguyen, Trong Kha, Hwang, Seong Oun, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Ruediger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Hwang, Seong Oun, editor, Tan, Syh Yuan, editor, and Bien, Franklin, editor

Published
2019
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9. HIT4Mal: Hybrid image transformation for malware classification.

Author

Vu, Duc‐Ly, Nguyen, Trong‐Kha, Nguyen, Tam V., Nguyen, Tu N., Massacci, Fabio, and Phung, Phu H.

Subjects
MACHINE performance, MACHINE learning, CLASSIFICATION, MALWARE prevention, IMAGE, MALWARE
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. [ABSTRACT FROM AUTHOR]

Published
2020
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