Visible to the public Defending IT Systems against Intelligent Malware

TitleDefending IT Systems against Intelligent Malware
Publication TypeConference Paper
Year of Publication2018
AuthorsKargaard, J., Drange, T., Kor, A., Twafik, H., Butterfield, E.
Conference Name2018 IEEE 9th International Conference on Dependable Systems, Services and Technologies (DESSERT)
Date Publishedmay
Keywordsadversarial examples, Antivirus Software vendors, ART, Classification algorithms, dynamic analysis, Gallium nitride, generative adversarial network, generative adversarial networks, Human Behavior, intelligent malware, invasive software, IT systems, learning (artificial intelligence), machine learning, machine learning algorithms, Malware, malware analysis, malware binaries, malware classification, malware detection, malware families, malware images, malware variants, Metrics, neural nets, privacy, pubcrawl, resilience, Resiliency, signatures, static analysis, Training, unsupervised deep neural networks
Abstract

The increasing amount of malware variants seen in the wild is causing problems for Antivirus Software vendors, unable to keep up by creating signatures for each. The methods used to develop a signature, static and dynamic analysis, have various limitations. Machine learning has been used by Antivirus vendors to detect malware based on the information gathered from the analysis process. However, adversarial examples can cause machine learning algorithms to miss-classify new data. In this paper we describe a method for malware analysis by converting malware binaries to images and then preparing those images for training within a Generative Adversarial Network. These unsupervised deep neural networks are not susceptible to adversarial examples. The conversion to images from malware binaries should be faster than using dynamic analysis and it would still be possible to link malware families together. Using the Generative Adversarial Network, malware detection could be much more effective and reliable.

URLhttps://ieeexplore.ieee.org/document/8409169
DOI10.1109/DESSERT.2018.8409169
Citation Keykargaard_defending_2018