ShanghaiTech University Knowledge Management System
SHIL: Self-Supervised Hybrid Learning for Security Attack Detection in Containerized Applications | |
2022 | |
会议录名称 | PROCEEDINGS - 2022 IEEE INTERNATIONAL CONFERENCE ON AUTONOMIC COMPUTING AND SELF-ORGANIZING SYSTEMS, ACSOS 2022 |
页码 | 41-50 |
发表状态 | 已发表 |
DOI | 10.1109/ACSOS55765.2022.00022 |
摘要 | Container security has received much research attention recently. Previous work has proposed to apply various machine learning techniques to detect security attacks in containerized applications. On one hand, supervised machine learning schemes require sufficient labelled training data to achieve good attack detection accuracy. On the other hand, unsupervised machine learning methods are more practical by avoiding training data labelling requirements, but they often suffer from high false alarm rates. In this paper, we present SHIL, a self-supervised hybrid learning solution, which combines unsupervised and supervised learning methods to achieve high accuracy without requiring any manual data labelling. We have implemented a prototype of SHIL and conducted experiments over 41 real world security attacks in 28 commonly used server applications. Our experimental results show that SHIL can reduce false alarms by 39-91% compared to existing supervised or unsupervised machine learning schemes while achieving a higher or similar detection rate. © 2022 IEEE. |
会议录编者/会议主办者 | Google ; IEEE Computer Society ; Protocol Labs ; Smart Contract Research Forum (SCRF) |
关键词 | Errors Learning systems Supervised learning Attack detection Container security Data labelling Hybrid learning Hybrid machine learning Learning schemes Security attack detection Security attacks Supervised machine learning Unsupervised machine learning |
会议名称 | 3rd IEEE International Conference on Autonomic Computing and Self-Organizing Systems, ACSOS 2022 |
出版地 | 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA |
会议地点 | Virtual, Online, United states |
会议日期 | September 19, 2022 - September 23, 2022 |
URL | 查看原文 |
收录类别 | EI ; CPCI-S |
语种 | 英语 |
资助项目 | NSA Science of Security Lablet: Impact through Research, Scientific Methods, and Community Development[H98230-17-D-0080] |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Information Systems ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000890265300005 |
出版者 | Institute of Electrical and Electronics Engineers Inc. |
EI入藏号 | 20224713157654 |
EI主题词 | Containers |
原始文献类型 | Conference article (CA) |
来源库 | IEEE |
引用统计 | 正在获取...
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文献类型 | 会议论文 |
条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/251431 |
专题 | 信息科学与技术学院_PI研究组_何静竹组 |
通讯作者 | Lin, Yuhang |
作者单位 | 1.North Carolina State Univ, Raleigh, NC 27695 USA 2.ShanghaiTech Univ, Shanghai, Peoples R China 3.Cisco, San Jose, CA USA |
推荐引用方式 GB/T 7714 | Lin, Yuhang,Tunde-Onadele, Olufogorehan,Gu, Xiaohui,et al. SHIL: Self-Supervised Hybrid Learning for Security Attack Detection in Containerized Applications[C]//Google, IEEE Computer Society, Protocol Labs, Smart Contract Research Forum (SCRF). 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA:Institute of Electrical and Electronics Engineers Inc.,2022:41-50. |
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