NDSS 2025 – ERW-Radar
好的,用户希望我用中文帮他总结一篇文章的内容,控制在100字以内,而且不需要特定的开头。首先,我需要仔细阅读文章内容,抓住主要信息。 文章标题是ERW-Radar,这是一个检测系统,用于对抗逃避型勒索软件。作者们提到现有的防御解决方案效果有限,因为勒索软件作者会模仿良性程序或减弱恶意行为。他们通过观察发现,逃避型勒索软件在加密过程中有独特的重复I/O行为,这在良性程序中很少见。此外,他们使用卡方检验和字节流概率分布来区分加密文件和良性修改文件。 接下来,他们提出了ERW-Radar系统,有三个突破点:上下文相关机制、细粒度内容分析机制和自适应机制。实验结果显示准确率96.18%,误报率5.36%,资源消耗较低。 总结时要涵盖系统名称、主要功能、检测方法和实验结果。确保不超过100字,并且直接描述内容。 </think> ERW-Radar是一种针对逃避型勒索软件的自适应检测系统,通过上下文行为检测和细粒度内容分析实现精准识别。该系统利用加密过程中的独特I/O行为模式及卡方检验、字节流概率分布等方法区分恶意文件,并结合自适应机制平衡检测准确性和效率。实验显示其检测准确率达96.18%,误报率为5.36%。 2026-1-26 16:0:0 Author: securityboulevard.com(查看原文) 阅读量:0 收藏

Authors, Creators & Presenters: Lingbo Zhao (Institute of Information Engineering, Chinese Academy of Sciences), Yuhui Zhang (Institute of Information Engineering, Chinese Academy of Sciences), Zhilu Wang (Institute of Information Engineering, Chinese Academy of Sciences), Fengkai Yuan (Institute of Information Engineering, CAS), Rui Hou (Institute of Information Engineering, Chinese Academy of Sciences)
PAPER
ERW-Radar: An Adaptive Detection System against Evasive Ransomware by Contextual Behavior Detection and Fine-grained Content Analysis
To evade existing antivirus software and detection systems, ransomware authors tend to obscure behavior differences with benign programs by imitating them or by weakening malicious behaviors during encryption. Existing defense solutions have limited effects on defending against evasive ransomware. Fortunately, through extensive observation, we find I/O behaviors of evasive ransomware exhibit a unique repetitiveness during encryption. This is rarely observed in benign programs. Besides, the $chi^2$ test and the probability distribution of byte streams can effectively distinguish encrypted files from benignly modified files. Inspired by these, we first propose ERW-Radar, a detection system, to detect evasive ransomware accurately and efficiently. We make three breakthroughs: 1) a contextual correlation mechanism to detect malicious behaviors; 2) a fine-grained content analysis mechanism to identify encrypted files; and 3) adaptive mechanisms to achieve a better trade-off between accuracy and efficiency. Experiments show that ERW-Radar detects evasive ransomware with an accuracy of 96.18% while maintaining a FPR of 5.36%. The average overhead of ERW-Radar is 5.09% in CPU utilization and 3.80% in memory utilization.
ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations’ YouTube Channel.

Permalink

*** This is a Security Bloggers Network syndicated blog from Infosecurity.US authored by Marc Handelman. Read the original post at: https://www.youtube-nocookie.com/embed/-h4vl94eq0U?si=XP-z7fYvmBGAij9B


文章来源: https://securityboulevard.com/2026/01/ndss-2025-erw-radar/
如有侵权请联系:admin#unsafe.sh