.png){kind=icon}
返回
Accurate and efficient exploit capture and classification
.png){kind=icon}
PRE
AI
DOI：10.1007/s11432-016-5521-0.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Software exploits, especially zero-day exploits, are major security threats. Every day, security experts discover and collect numerous exploits from honeypots, malware forensics, and underground channels. However, no easy methods exist to classify these exploits into meaningful categories and to accelerate diagnosis as well as detailed analysis. To address this need, we present SeismoMeter, which recognizes both control-flowhijacking, and data-only attacks by combining approximate control-flow integrity, fast dynamic taint analysis and API sandboxing schemes. Once it detects an exploit incident, SeismoMeter generates a succinct data representation, called an exploit skeleton, to characterize the captured exploit. SeismoMeter then classifies the captured exploits into different exploit families by performing distance computing on the extracted skeletons. To evaluate the efficiency of SeismoMeter, we conduct a field test using exploit samples from public exploit databases, such as Metasploit, as well as wild-captured exploits. Our experiments demonstrate that SeismoMeter is a practical system that successfully detects and correctly classifies all these exploit attacks.
Keyword:
software security
exploit classification
exploit attack capture
control flow integrity
JIT security
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
4.9K
被引数:
8.9K
机构
引用论文
分享收藏Abnormalities in neuromuscular junction structure and skeletal muscle function in mice lacking the P2X2 nucleotide receptor.png)
Neuroscience
OAAI
NeuroscienceIF0
OAAI分享收藏
查看更多内容