Optimization of code caves in malware binaries to evade machine learning detectors-Reference-Cited by-同舟云学术

Optimization of code caves in malware binaries to evade machine learning detectors

Published:2022-05 Issue: Volume:116 Page:102643
ISSN:0167-4048
Container-title:Computers & Security
language:en
Short-container-title:Computers & Security

Author:

Yuste Javier^ORCID,Pardo Eduardo G.^ORCID,Tapiador Juan

Publisher

Elsevier BV

Subject

Law,General Computer Science

Reference66 articles.

1. When malware is packin’heat; limits of machine learning classifiers based on static analysis features;Aghakhani,2020

2. Predicting the growth of total number of users, devices and epidemics of malware in internet based on analysis of statistics with the detection of near-periodic growth features;Aleshkin,2019

3. High intrinsic dimensionality facilitates adversarial attack: theoretical evidence;Amsaleg;IEEE Trans. Inf. Forensics Secur.,2021

4. Evading machine learning malware detection;Anderson;Black Hat,2017

5. Ember: an open dataset for training static pe malware machine learning models;Anderson;arXiv preprint arXiv:1804.04637,2018

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