谷歌浏览器插件
订阅小程序
在清言上使用

Coda: Runtime Detection of Application-Layer CPU-Exhaustion DoS Attacks in Containers

IEEE Transactions on Services Computing(2023)

引用 3|浏览0
暂无评分
摘要
Denial of service (DoS) attacks have increasingly exploited vulnerabilities in algorithms or implementation methods in application-layer programs. In this type of attack, called CPU-exhaustion DoS attack, a few well-crafted requests may consume a lot of server resources, which is essentially different from traditional volumetric DoS attacks. Due to the lack of recognizable patterns, the traditional network-layer defense mechanism is usually unable to detect such sophisticated DoS attacks. In this article, we propose Coda, a framework for detecting application-layer CPU-exhaustion DoS attacks in containers. Coda monitors the CPU time consumed by each connection and uses statistical methods to detect attacks. It traces system calls and other related information from the container based on Linux eBPF at the host level. Some specific system calls are used to indicate the establishment and closure of the connection, which in turn indicate the start/end of the request processing. After triggering these specific system calls, Coda starts/ends monitoring the CPU time consumed by a connection. An attack can be detected when the CPU time consumed by an attack connection is statistically different from that consumed by a legitimate connection. Coda has the following key advantages. First, it works with programs built in different programming languages. Second, it remains agnostic to the source code of protected programs. Third, it supports monitoring the container and is transparent to the container. Through evaluation of real-world attacks, we demonstrate that Coda can accurately detect ongoing application-layer CPU-exhaustion DoS attacks with low additional overhead.
更多
查看译文
关键词
Containers,Monitoring,Denial-of-service attack,Codes,Training,Servers,Runtime,Detection,CPU-exhaustion DoS attack,Index Terms,container,eBPF
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要