摘要
随着反检测技术的不断发展,产生了大量形态多样的恶意代码变种,传统检测技术已无法准确检测出该种未知恶意代码。由于数据可视化方法能将恶意代码的核心表现在图像特征中,因此可视化恶意代码检测方法受到越来越多关注。首先对传统恶意代码检测技术进行概括总结,然后介绍当前主流的恶意代码可视化方法,接着分析了基于恶意代码图像的机器学习与深度学习检测方法,具体涵盖了该方法所用的模型结构、创新点及评估结果,最后对当前检测技术所面临的问题进行总结,并阐述了未来可能的研究方向,旨在助力恶意代码检测技术的发展。
With the continuous development of anti-detection technology,a large number of diversified malicious code variants have been produced,traditional detection technology has been unable to accurately detect this unknown malicious code.Because data visualization meth⁃ods can express the core of malicious code in image features,Therefore,the visualized malicious code detection method has received more and more attention.First,summarize the traditional malicious code detection technology,and then introduces the current mainstream malicious code visualization methods,and then analyzes the machine learning and deep learning detection methods based on malicious code images,which specifically covers the model structure,innovation points and evaluation method used in the method.Finally,summarize the problems faced by the current detection technology,and explain the possible future research directions,aiming to help the development of malicious code detection technology.
作者
李豪
钱丽萍
LI Hao;QIAN Li-ping(School of Electrical and Information Engineering,Beijing University of Civil Engineering and Architecture,Beijing 100044,China)
出处
《软件导刊》
2022年第5期9-16,共8页
Software Guide
基金
国家自然科学基金项目(61571144)。
关键词
反检测技术
恶意代码
数据可视化
机器学习
深度学习
anti-detection technology
malicious code
data visualization
machine learning
deep learning
