Dr. Justin Zhan Profile

Dr. Justin Zhan

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Proceedings Article | 31 May 2022 Presentation + Paper

Enforcing feature correlation on cycle-consistent GAN generated functions: a first step in closing the synthetic measured gap found in SAR images

Matthew Swan, Anne Major, Jacob Lear, Caleb Parks, Justin Zhan

Proceedings Volume 12095, 120950B (2022) https://doi.org/10.1117/12.2624110

KEYWORDS: Synthetic aperture radar, Algorithm development, Automatic target recognition, Neural networks, Machine learning, Image processing

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Due to their ability to capture images in a variety of environmental conditions, Synthetic Aperture Radars (SAR) are of particular interest in the automatic target recognition (ATR) domain. In order to develop SARATR machine learning (ML) algorithms, a large sample set indicative of the underlying population must be used. This is an issue since gathering SAR images, even for a single target, is an expensive and time consuming process. Recently a data set, known as the SAMPLE data set, consisting of synthetic SAR samples has been released. Ideally theses synthetic images can be used in place of real SAR samples. Unfortunately, training SAR-ATR ML algorithms with samples exclusively from the SAMPLE data set produces algorithms with poor performance on real SAR images. This paper is focused on creating new variants of cycle-consistent generative adversarial networks (CycleGAN) to produce a transformation function that maps a synthetic SAR image to a useful approximation of a real SAR image. By introducing a new feature correlation module to the cycle consistent GAN architecture we take the first steps in closing the gap between synthetic SAR images and measured SAR images.

Proceedings Article | 12 April 2021 Presentation + Paper

Adversarial attacks against image-based malware detection using autoencoders

Alycia Carey, Huy Mai, Justin Zhan, Asif Mehmood

Proceedings Volume 11735, 117350A (2021) https://doi.org/10.1117/12.2587923

KEYWORDS: Safety, Network architectures, Computer programming, Classification systems, Avionic systems, Analytical research

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Over the past decade, deep learning approaches have been applied to the detection of malicious software, otherwise known as malware. Despite their improved performance compared to conventional detection methods such as static and dynamic analysis, however, deep learning-based malware detection systems have been shown to be vulnerable to adversarial attacks. Few image-based malware detection systems have been proposed, especially those that evaluate their performance against adversarial attacks. Furthermore, little research has been done beyond the classification of malware targeted at Windows (PE) or Android systems, leaving entire realms such as Mac (Mach-O), Linux (ELF), and embedded software unexplored and unprotected. These realms, specifically embedded software, are used in critical technology such as avionic systems and special care must be taken to ensure their safety. In this paper, we present an image-based malware detection system on PE, ELF, Mach- O, and embedded C code files. The system’s architecture incorporates layers of encoders that are taken from independently-trained autoencoders and multi-layer perceptron that returns the output of the network. We evaluate the performance of the system against adversarial attacks, or the misclassification of a malware file as a benign, by adding gradient based perturbations to unused sections of the malware often referred to as the slack bits. The network achieves an accuracy of 96.51% on non-adversarial PE and ELF files, 95.45% on transfer learned non-adversarial Mach-O files, and 99.2% on transfer learned non-adversarial synthetic plane files. For the classification of adversarial examples, the network achieved a 81% success rate of misclassification on adversarial PE and ELF files and a 99% success rate of misclassification on adversarial synthetic plane files.

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