.png){kind=icon}
返回
ContainerGuard: A Real-Time Attack Detection System in Container-Based Big Data Platform
.png){kind=icon}
PRE
AI
DOI：10.1109/TII.2020.3047416.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
As a lightweight, flexible, and high-performance operating system virtualization, containers are used to speed up the big data platform. However, due to the imperfection of the resource isolation mechanism and the property of shared kernel, the meltdown and spectre attacks can lead to information leakage of kernel space and coresident containers. In this article, a noise-resilient and real-time detection system, named ContainerGuard, is proposed to detect meltdown and spectre attacks in the container-based big data platform. ContainerGuard uses a nonintrusive manner to collect lifecycle multivariate time-series performance event data of processes in containers and then uses ensemble of variational autoencoders as generative neural networks to learn the robust representations of normal patterns. Therefore, ContainerGuard meets the urgent need for information protection in the container-based big data platform. Our evaluations using real-world datasets show that ContainerGuard achieves excellent detection performance and only introduces about 4.5% of running performance overhead to the platform.
Keyword:
Containers
Big Data
Process control
Side-channel attacks
Kernel
Security
Hardware
Anomaly detection
big data platform security
container
meltdown and spectre
variational autoencoder (VAE)
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:
9.9
论文数:
8.6K
被引数:
6.0W
机构
引用论文
Anomaly Detection in Real-Time Multi-Threaded Processes Using Hardware Performance Counters使用硬件性能计数器在实时多线程进程中进行异常检测
PREAI
分享收藏
A dual-parameter sensor for strain and temperature measurement featuring cascaded LPFG-FP structure.png)
Optik
OptikIF0
PREAI
分享收藏
查看更多内容