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Type: Conference paper
Title: Statically Detecting Adversarial Malware through Randomised Chaining
Author: Crawford, M.
Wang, W.
Sun, R.
Xue, M.
Citation: Proceedings of the ACM International Conference Proceeding Series (ACSW, 2022), 2022, pp.91-95
Publisher: Association for Computing Machinery
Publisher Place: Online
Issue Date: 2022
ISBN: 9781450396066
Conference Name: Australasian Computer Science Week (ACSW) (14 Feb 2022 - 17 Feb 2022 : Virtual Online)
Statement of
Matthew Crawford, Wei Wang, Ruoxi Sun, Minhui Xue
Abstract: With the rapid growth of malware attacks, more antivirus developers consider deploying machine learning technologies into their productions. Researchers and developers published various machine learning-based detectors with high precision on malware detection in recent years. Although numerous machine learningbased malware detectors are available, they face various machine learning-targeted attacks, including evasion and adversarial attacks. This project explores how and why adversarial examples evade malware detectors, then proposes a randomised chaining method to defend against adversarial malware statically. This research is crucial for working towards combating the pertinent malware cybercrime.
Rights: © 2022 Association for Computing Machinery. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from
DOI: 10.1145/3511616.3513103
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Appears in Collections:Computer Science publications

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