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Are We Protected Yet? Developing a Machine Learning Detection System to Combat Zero-Day Malware Attacks

机译：我们受到保护了吗？开发机器学习检测系统以对抗零日恶意软件攻击

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作者
Miller, Michael Glen;
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作者单位

Utica College;

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授予单位 Utica College;
学科
学位 M.S.
年度 2018
页码 71 p.
总页数 71
原文格式 PDF
正文语种 eng
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1. Automated poisoning attacks and defenses in malware detection systems: An adversarial machine learning approach [J] . Sen Chen, Minhui Xue, Lingling Fan, Computers & Security . 2018,第MARa期

机译：恶意软件检测系统中的自动中毒攻击和防御：对抗性机器学习方法
2. Performance Evaluation of Machine Learning Algorithms for Detection and Prevention of Malware Attacks [J] . Emmanuel Gbenga Dada, Joseph Stephen Bassi, Yakubu Joseph Hurcha, IOSR journal of computer engineering . 2019,第3期

机译：用于检测和预防恶意软件攻击的机器学习算法的性能评估
3. Robust Android Malware Detection System Against Adversarial Attacks Using Q-Learning [J] . Rathore Hemant, Sahay Sanjay K., Nikam Piyush, Information systems frontiers . 2021,第4期

机译：强大的Android恶意软件检测系统反对使用Q-Learning的对抗攻击
4. When Machine Learning Meets Hardware Cybersecurity: Delving into Accurate Zero-Day Malware Detection [C] . Zhangying He, Tahereh Miari, Hosein Mohammadi Makrani, International Symposium on Quality Electronic Design . 2021

机译：当机器学习符合硬件网络安全时：深入计入准确的零恶意软件检测
5. Detection and Defense of Cyber Attacks on the Machine Learning Control of Robotic Systems [D] . Clark Jr, George W., Jr. 2019

机译：对机器人系统机器学习控制的网络攻击检测与防御
6. Machine learning through cryptographic glasses: combating adversarial attacks by key-based diversified aggregation [O] . Olga Taran, Shideh Rezaeifar, Taras Holotyak, -1

机译：通过密码眼镜进行机器学习：通过基于密钥的多样化聚合来对抗对抗性攻击
7. Automated Poisoning Attacks and Defenses in Malware Detection Systems: An Adversarial Machine Learning Approach [O] . Chen, Sen, Xue, Minhui, Fan, Lingling, 2017

机译：恶意软件检测系统中的自动中毒攻击和防御：对抗机器学习方法

Are We Protected Yet? Developing a Machine Learning Detection System to Combat Zero-Day Malware Attacks

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