Generation of Adversarial Malware Based on Genetic Algorithm and Instruction Replacement
Abstract
References
Index Terms
- Generation of Adversarial Malware Based on Genetic Algorithm and Instruction Replacement
Recommendations
Gradient-Based Adversarial Attacks Against Malware Detection by Instruction Replacement
Wireless Algorithms, Systems, and ApplicationsAbstractDeep learning plays a vital role in malware detection. The Malconv is a well-known deep learning-based open source malware detection framework and is trained on raw bytes for malware binary detection. Researchers propose adversarial example ...
Adversarial malware sample generation method based on the prototype of deep learning detector
Highlights- Present a novel approach for generating adversarial malware based on prototype of the malware detection model.
AbstractThe deep learning methods had been proved to be effective for malware detection in the past. However, the recent studies show that deep learning models are vulnerable to adversarial attacks. Thus, the malware detection models based on ...
Breaking the Anti-malware: EvoAAttack Based on Genetic Algorithm Against Android Malware Detection Systems
Computational Science – ICCS 2023AbstractToday, android devices like smartphones, tablets, etc., have penetrated very deep into our modern society and have become an integral part of our daily lives. The widespread adoption of these devices has also garnered the immense attention of ...
Comments
Information & Contributors
Information
Published In

Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- the Guangdong Basic and Applied Basic Research Foundation under Grant 2021A1515012297, Guangdong Key Laboratory of Data Security and Privacy Preserving (Grant No. 2017B030301004), and the Open Project of Guangdong Provincial Key Laboratory of High-Performance Computing (2021).
Conference
Acceptance Rates
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 61Total Downloads
- Downloads (Last 12 months)45
- Downloads (Last 6 weeks)6
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format