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Security and Communication Networks
Volume 2019, Article ID 8485365, 9 pages
https://doi.org/10.1155/2019/8485365
Research Article

Malware Detection on Byte Streams of PDF Files Using Convolutional Neural Networks

SCH Media Labs, Soonchunhyang University, Asan 31538, Republic of Korea

Correspondence should be addressed to Ah Reum Kang; ten.gnakra@kmra

Received 25 January 2019; Accepted 11 March 2019; Published 3 April 2019

Guest Editor: Pelin Angin

Copyright © 2019 Young-Seob Jeong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

With increasing amount of data, the threat of malware keeps growing recently. The malicious actions embedded in nonexecutable documents especially (e.g., PDF files) can be more dangerous, because it is difficult to detect and most users are not aware of such type of malicious attacks. In this paper, we design a convolutional neural network to tackle the malware detection on the PDF files. We collect malicious and benign PDF files and manually label the byte sequences within the files. We intensively examine the structure of the input data and illustrate how we design the proposed network based on the characteristics of data. The proposed network is designed to interpret high-level patterns among collectable spatial clues, thereby predicting whether the given byte sequence has malicious actions or not. By experimental results, we demonstrate that the proposed network outperform several representative machine-learning models as well as other networks with different settings.