A Feasibility Study on Evasion Attacks Against NLP-Based Macro Malware Detection Algorithms
Author:
Affiliation:
1. National Defense Academy of Japan, Yokosuka, Japan
2. Japan Ground Self-Defense Force, Shinjuku-ku, Japan
Funder
JSPS KAKENHI
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10345584.pdf?arnumber=10345584
Reference45 articles.
1. Tutorial: An Overview of Malware Detection and Evasion Techniques
2. Quantifying the impact of adversarial evasion attacks on machine learning based android malware classifiers
3. Adversarial Examples for CNN-Based Malware Detectors
4. Automated poisoning attacks and defenses in malware detection systems: An adversarial machine learning approach
5. Easy to fool? Testing the anti-evasion capabilities of PDF malware scanners;Ehteshamifar;arXiv:1901.05674,2019
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