ZeVigilante: Detecting Zero-Day Malware Using Machine Learning and Sandboxing Analysis Techniques.

导出 LinkOut

翻译打印收藏纠错

作者： Fahd,Alhaidari ^[1] ; Nouran Abu,Shaib ^[2] ; Maram,Alsafi ^[2] ; Haneen,Alharbi ^[2] ; Majd,Alawami ^[2] ; Reem,Aljindan ^[2] ; Atta-Ur,Rahman ^[2] ; Rachid,Zagrouba ^[3]

作者单位： Saudi Aramco Cybersecurity Chair, Dhahran, Saudi Arabia. ^[1] Department of Networks and Communications, College of Computer Science and Information Technology (CCSIT), Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia. ^[2] Department of Computer Science, College of Computer Science and Information Technology (CCSIT), Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia. ^[3] Department of Computer Information Systems, College of Computer Science and Information Technology (CCSIT), Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia. ^[4]

主题词算法(Algorithms);贝叶斯定理(Bayes Theorem);软件(Software)

DOI 10.1155/2022/1615528

PMID 35586085

发布时间 2022-05-20

翻译满意度评价：

准确一般字义偏差语意偏离译文作用小提交

浏览2

Computational intelligence and neuroscience

2022年2022卷

1615528页

相似文献

中文期刊
外文期刊
学位论文
会议论文

检索历史清除

ZeVigilante: Detecting Zero-Day Malware Using Machine Learning and Sandboxing Analysis Techniques.

Computational intelligence and neuroscience

相似文献

Computational intelligence and neuroscience

相关期刊

参考文献：
指定格式：	NoteExpress EndNote RefWorks NoteFirst

检索历史 清除

ZeVigilante: Detecting Zero-Day Malware Using Machine Learning and Sandboxing Analysis Techniques.

Computational intelligence and neuroscience

相似文献

Computational intelligence and neuroscience

相关期刊

检索历史清除