Arms Race in Adversarial Malware Detection: A Survey
Author:
Affiliation:
1. Nanjing University of Science and Technology, Nanjing, Jiangsu, China
2. Case Western Reserve University, Notre Dame, IN, USA
3. University of Colorado Colorado Springs, Colorado Springs, Colorado, USA
Abstract
Funder
National Key R&D Program of China
Jiangsu Province Modern Education Technology Research Project
National Vocational Education Teacher Enterprise Practice Base
“Integration of Industry and Education” Special Project
NSF
ARO
Colorado State Bill
Publisher
Association for Computing Machinery (ACM)
Subject
General Computer Science,Theoretical Computer Science
Link
https://dl.acm.org/doi/pdf/10.1145/3484491
Reference143 articles.
1. Adversarial Deep Learning for Robust Detection of Binary Encoded Malware
2. Yara;Alvarez Victor M.;(May 2019). Retrieved May 2, 2019 from http://virustotal.github.io/yara/.,2019
3. Improving malware classification
4. Hyrum S. Anderson Anant Kharkar Bobby Filar David Evans and Phil Roth. 2018. Learning to evade static PE machine learning malware models via reinforcement learning. arXiv:1801.08917. Retrieved from https://arxiv.org/abs/1801.08917.
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