【恶软】【对抗】【攻】Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables
作者是Bojan Kolosnjaji, Ambra Demontis等 2018年发表在European Signal Processing Conference 一、主要内容： 在本文中，我们调查了使用深层网络从原始字节中学习的恶意软件检测方法的漏洞。 我们提出了一种基于梯度的攻击，这种攻击可以通过在每个恶意软件样本的末尾仅更改几个特定的字节，同时保留其侵入功能，来逃避深度网络检测。 结果
相关文章
- 1. 【论文笔记】DeepOrigin: End-to-End Deep Learning for Detection of New Malware Families
- 2. Using Markov Chains for Android Malware Detection
- 3. Demadroid: Object Reference Graph-Based Malware Detection in Android
- 4. Machine Learning for Encrypted Malware Traffic Classification
- 5. 对抗机器学习：Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples
- 6. 四篇关于恶意软件对抗方面的paper要点
- 7. 对A Multimodal Deep Learning Method for Android Malware Detection Using Various Features的简单理解
- 8. On the Security of Machine Leanring in Malware C&C Detection: A Survey
- 9. 【论文笔记】Malware Detection with Deep Neural Network Using Process Behavior
- 10. 对抗机器学习：Generating Adversarial Malware Examples for Black-box Attacks Based on GAN
- 更多相关文章...
- • Swift for-in 循环 - Swift 教程
- • XSL-FO 软件 - XSL-FO 教程
- • Java Agent入门实战（一）-Instrumentation介绍与使用
- • Kotlin学习（一）基本语法
相关标签/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
欢迎关注本站公众号,获取更多信息
相关文章
- 1. 【论文笔记】DeepOrigin: End-to-End Deep Learning for Detection of New Malware Families
- 2. Using Markov Chains for Android Malware Detection
- 3. Demadroid: Object Reference Graph-Based Malware Detection in Android
- 4. Machine Learning for Encrypted Malware Traffic Classification
- 5. 对抗机器学习：Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples
- 6. 四篇关于恶意软件对抗方面的paper要点
- 7. 对A Multimodal Deep Learning Method for Android Malware Detection Using Various Features的简单理解
- 8. On the Security of Machine Leanring in Malware C&C Detection: A Survey
- 9. 【论文笔记】Malware Detection with Deep Neural Network Using Process Behavior
- 10. 对抗机器学习：Generating Adversarial Malware Examples for Black-box Attacks Based on GAN