基于无监督系统调用规则生成的容器云实时异常检测系统

doi:10.3969/j.issn.1671-1122.2023.12.009

摘要/Abstract

摘要：

容器技术是目前云计算领域的主流技术之一，与虚拟机相比，容器具有启动速度快、可移植性高、扩展能力强等优势。然而，更低的资源隔离性和共享内核的特性给容器和云平台引入了新的安全风险，容易导致资源侵占、数据泄露，宿主机被劫持等问题。为实现容器云平台安全与可观测性，文章提出了一种基于无监督系统调用过滤规则生成的容器云实时异常检测系统，首先，通过无代理模式采集集群中容器的系统调用行为数据；然后，通过一种适用于系统调用数据且关注具体参数的方法在线挖掘过滤规则模板；最后，将挖掘得到的原始规则模板适配至具体规则检测引擎并更新，实现实时异常检测。实验结果表明，该系统可正确挖掘较为精确的系统调用规则模板并转换为具体检测规则，其检测效果与人工编写基本一致。

关键词: 异常检测, 容器安全, 系统调用, 规则生成

Abstract:

Container technology is currently one of the mainstream technologies in cloud computing. Compared with virtual machines, containers have significant advantages such as fast startup, high portability, and high scalability. However, the lower resource isolation and shared kernel characteristics introduce new security risks to containers and cloud platforms, which can easily lead to serious threats such as resource appropriation, data leakage, and host hijacking. To achieve security and observability of container cloud platform, this paper proposed a container cloud real-time anomaly detection system based on unsupervised system call filtering rule generation, which collected system call behavior data of containers in the cluster through agentless mode, then mined filtering rules online through a method that applied to system call data and focuses on specific parameters, and finally adapted the original rules to specific rule engines, thus achieving real-time anomaly detection. The experimental results show that this system can correctly mine comparatively accurate syscall templates and convert them into corresponding detection rules, and the detection effect is basically consistent with manually written rules.

Key words: anomaly detection, container security, system call, rule generation

中图分类号:

TP309

吴圣麟, 刘汪根, 严明, 吴杰. 基于无监督系统调用规则生成的容器云实时异常检测系统[J]. 信息网络安全, 2023, 23(12): 91-102.

WU Shenglin, LIU Wanggen, YAN Ming, WU Jie. A Real-Time Anomaly Detection System for Container Clouds Based on Unsupervised System Call Rule Generation[J]. Netinfo Security, 2023, 23(12): 91-102.

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参考文献 43

[1]	BERNSTEIN D. Containers and Cloud: From LXC to Docker to Kubernetes[J]. IEEE Cloud Computing, 2014, 1(3): 81-84. doi: 10.1109/MCC.2014.51 URL
[2]	LIN X, LEI L, WANG Y, et al. A Measurement Study on Linux Container Security: Attacks and Countermeasures[C]// ACM. Proceedings of the 34th Annual Computer Security Applications Conference. New York: ACM, 2018: 418-429.
[3]	KERRISK M. Linux Manual Pages: Section 7[EB/OL]. [2023-08-10]. https://man7. org/linux/man-pages/dir_section_7. html.
[4]	MARTIN A, RAPONI S, COMBE T, et al. Docker Ecosystem- Vulnerability Analysis[J]. Computer Communications, 2018, 122: 30-43. doi: 10.1016/j.comcom.2018.03.011 URL
[5]	Tencent Cloud Computing(Beijing) Co., Ltd. Container Security Whitepaper of Tencent Cloud[EB/OL]. (2021-04-01)[2023-04-10]. https://cloud.tencent.com/developer/article/1639379?from=15425.
	腾讯云计算北京有限责任公司. 腾讯云容器安全白皮书[EB/OL]. (2021-04-01)[2023-04-10]. https://cloud.tencent.com/developer/article/1639379?from=15425.
[6]	LIU M, XUE Z, XU X, et al. Host-Based Intrusion Detection System with System Calls: Review and Future Trends[J]. ACM Computing Surveys, 2019, 51(5): 1-36.
[7]	CASTANHEL G R, HEINRICH T, CESCHIN F, et al. Taking a Peek: An Evaluation of Anomaly Detection Using System Calls for Containers[C]// IEEE. 2021 IEEE Symposium on Computers and Communications (ISCC). New York: IEEE, 2021: 1-6.
[8]	MOORE P, SMALLEY S, PARIS E. Linux Security Module Usage[EB/OL]. [2023-08-11]. https://www.kernel.org/doc/html/v4.16/admin-guide/LSM/index.html.
[9]	HUANG C, WANG K, LI Y, et al. ASPGen-D: Automatically Generating Fine-Grained Apparmor Policies for Docker[C]// IEEE. 2022 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom). New York: IEEE, 2022: 822-829.
[10]	ZHU H, GEHRMANN C. Kub-Sec, An Automatic Kubernetes Cluster AppArmor Profile Generation Engine[C]// IEEE. 2022 14th International Conference on COMmunication Systems & NETworkS (COMSNETS). New York: IEEE, 2022: 129-137.
[11]	IIGUNI T, KAMEI H, SAISHO K. Sprofiler: Automatic Generating System of Container-Native System Call Filtering Rules for Attack Surface Reduction[C]// IEEE. 2021 International Conference on Computational Science and Computational Intelligence (CSCI). New York: IEEE, 2021: 704-709.
[12]	SULTAN S, AHMAD I, DIMITRIOU T. Container Security: Issues, Challenges, and the Road Ahead[J]. IEEE Access, 2019, 7: 52976-52996. doi: 10.1109/Access.6287639 URL
[13]	GRIMMER M, RÖHLING M, KREUSEL D, et al. A Modern and Sophisticated Host Based Intrusion Detection Data Set[J]. IT-Sicherheit Als Voraussetzung Für Eine Erfolgreiche Digitalisierung, 2019: 135-145.
[14]	FALCOSECURITY. Falco[EB/OL]. (2023-04-06)[2023-04-10]. https://falco.org.
[15]	AQUASECURITY. Tracee[EB/OL]. (2023-04-01)[2023-04-10]. https://github.com/aquasecurity/tracee.
[16]	GHAVAMNIA S, PALIT T, BENAMEUR A. Confine: Automated System Call Policy Generation for Container Attack Surface Reduction[EB/OL]. (2020-10-03)[2023-04-10]. https://xueshu.baidu.com/usercenter/paper/show?paperid=1s1f0js0wg1b0pm0k55v0p50c9316847&site=xueshu_se.
[17]	CANELLA C, WERNER M, GRUSS D, et al. Automating Seccomp Filter Generation for Linux Applications[C]// ACM. Proceedings of the 2021 on Cloud Computing Security Workshop. New York: ACM, 2021: 139-151.
[18]	MATTETTI M, SHULMAN-PELEG A, ALLOUCHE Y, et al. Securing the Infrastructure and the Workloads of Linux Containers[C]// IEEE. 2015 IEEE Conference on Communications and Network Security (CNS). New York: IEEE, 2015: 559-567.
[19]	LOUKIDIS-ANDREOU F, GIANNAKOPOULOS I, DOKA K, et al. Docker-Sec: A Fully Automated Container Security Enhancement Mechanism[C]// IEEE. 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS). New York: IEEE, 2018: 1561-1564.
[20]	ZHU H, GEHRMANN C. Lic-Sec: An Enhanced App Armor Docker Security Profile Generator[J]. Journal of Information Security and Applications, 2021, 61: 102924-102931. doi: 10.1016/j.jisa.2021.102924 URL
[21]	TUNDE-ONADELE O, HE J, DAI T, et al. A Study on Container Vulnerability Exploit Detection[C]// IEEE. 2019 IEEE International Conference on Cloud Engineering (IC2E). New York: IEEE, 2019: 121-127.
[22]	LI Xiaohan, ZHANG Xinyou. Docker Anomaly Detection Based on System Call[J]. Microelectronics & Computer, 2022, 39(12): 77-85.
	栗晓晗, 张新有. 基于系统调用的 Docker 容器异常检测[J]. 微电子学与计算机, 2022, 39(12): 77-85.
[23]	TIEN C, HUANG T, TIEN C, et al. KubAnomaly: Anomaly Detection for the Docker Orchestration Platform with Neural Network Approaches[J]. Engineering Reports, 2019, 1(5): 80-92.
[24]	Github. Sysdig[EB/OL]. (2023-02-20)[2023-04-10]. https://github.com/draios/sysdig.
[25]	HE P, ZHU J, ZHENG Z, et al. Drain: An Online Log Parsing Approach with Fixed Depth Tree[C]// IEEE. 2017 IEEE International Conference on Web Services (ICWS). New York: IEEE, 2017: 33-40.
[26]	LI X, CHEN P, JING L, et al. SwissLog: Robust and Unified Deep Learning Based Log Anomaly Detection for Diverse Faults[C]// IEEE. 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE). New York: IEEE, 2020: 92-103.
[27]	ZHU Yuqian, DENG Jiaying, PU Jiachen, et al. ML-Parser: An Efficient and Accurate Online Log Parser[J]. Journal of Computer Science and Technology, 2022, 37(6): 1412-1423. doi: 10.1007/s11390-021-0730-4
[28]	ZHU J, HE S, LIU J, et al. Tools and Benchmarks for Automated Log Parsing[C]// IEEE/ACM. 2019 IEEE/ACM 41st International Conference on Software Engineering:Software Engineering in Practice (ICSE-SEIP). New York: IEEE, 2019: 121-130.
[29]	LUPTON S, WASHIZAKI H, YOSHIOKA N, et al. Literature Review on Log Anomaly Detection Approaches Utilizing Online Parsing Methodology[C]// IEEE. 2021 28th Asia-Pacific Software Engineering Conference (APSEC). New York: IEEE, 2021: 559-563.
[30]	WANG J, DONG Y. Measurement of Text Similarity: A Survey[J]. Information, 2020, 11(9): 421-438. doi: 10.3390/info11090421 URL
[31]	PRASETYA D, PRASETYA W, HIRASHIMA T. The Performance of Text Similarity Algorithms[J]. International Journal of Advances in Intelligent Informatics, 2018, 4(1): 63-71. doi: 10.26555/ijain.v4i1.152 URL
[32]	NORA RAJU T, RAHANA P A, MONCY R, et al. Sentence Similarity-A State of Art Approaches[C]// IEEE. 2022 International Conference on Computing, Communication, Security and Intelligent Systems (IC3SIS). New York: IEEE, 2022: 1-6.
[33]	PRADHAN N, GYANCHANDANI M, WADHVANI R. A Review on Text Similarity Technique Used in IR and Its Application[J]. International Journal of Computer Applications, 2015, 120(9): 29-34.
[34]	CHRISTEN P. A Comparison of Personal Name Matching: Techniques and Practical Issues[C]// IEEE. Sixth IEEE International Conference on Data Mining-Workshops (ICDMW’06). New York: IEEE, 2006: 290-294.
[35]	DU M, LI F, ZHENG G, et al. DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning[C]// ACM. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. New York: ACM, 2017: 1285-1298.
[36]	GANTIKOW H, REICH C, KNAHL M, et al. Rule-Based Security Monitoring of Containerized Environments[J]. Cloud Computing and Services Science, 2020, 1218(8): 66-86.
[37]	GUO R, ZENG J. Phantom Attack: Evading System Call Monitoring[EB/OL]. [2023-04-10]. https://connect.ed-diamond.com/misc/misc-130/introduction-a-tetragon-observabilite-et-securite-temps-reel-basees-sur-ebpf.
[38]	Massachusetts Institute of Technology Lincoln Laboratory. DARPA Intrusion Detection Evaluation Data Set[EB/OL]. (1999-05-12)[2023-05-10]. https://www.ll.mit.edu/ideval/data/1999data.html.
[39]	University of New Mexico Computer Science Department Farris Engineering Center. Computer Immune Systems-Data Sets and Software[EB/OL]. [2023-03-10]. https://www.cs.unm.edu/-immsec/systemcalls.htm.
[40]	CREECH G, HU J. Generation of a New IDS Test Dataset: Time to Retire the KDD Collection[C]// IEEE. 2013 IEEE Wireless Communications and Networking Conference (WCNC). New York: IEEE, 2013: 4487-4492.
[41]	HAIDER W, HU J, SLAY J, et al. Generating Realistic Intrusion Detection System Dataset Based on Fuzzy Qualitative Modeling[J]. Journal of Network and Computer Applications, 2017, 87: 185-192. doi: 10.1016/j.jnca.2017.03.018 URL
[42]	GRIMMER M. LID-DS[EB/OL]. (2021-04-05)[2023-03-10]. https://github.com/LID-DS/LID-DS.
[43]	WU S. Dataset Recaptured from the Scenarios in LID-DS[EB/OL]. (2023-02-01)[2023-03-10]. https://github.com/wsl-fd/dataset_RADSCCBUSCRG.

研究	规则引擎	在线生成	热更新	规则适用范围	适用于容器云集群
文献[16]方案	Seccomp	×	×	容器的系统调用类型	×
文献[17]方案	Seccomp	×	×	容器的系统调用类型	×
文献[18]方案	AppArmor	×	×	Docker守护进程的部分系统调用类型及特定资源	×
文献[19]方案	AppArmor	√	×	容器与Docker守护进程的系统调用Capability与网络相关规则	×
文献[20]方案	AppArmor	√	√	容器与Docker守护进程的系统调用Capability与文件、网络相关规则	×

基本字段		描述
template_info		模板自身基本信息，包括id、更新时间等
syscall_type		系统调用类型信息，包括类型名称与方向（进入或返回），此字段为枚举值，不参与模板挖掘
image_info		容器镜像相关信息，包括镜像名称和版本
process_info		进程相关信息，包括进程名、进程路径、进程参数及父进程相应信息
event_action	action_type	事件操作信息，包含操作类型（如tcp、file）、操作选项（如打开文件时只读、读写模式）、操作目标列表（如具体打开的文件路径、IP地址，采用列表面对rename等涉及多对象的操作），其中类型与选项为枚举值，不参与模板挖掘
	action_opt
	action_subjects
event_results		事件结果列表，包括返回值、数据大小等
event_data		事件相关数据，例如，write调用所写内容

场景	模板总数			总损失
场景	本算法	Drain	SwissLog	本算法	Drain	SwissLog
CVE-2017-7529	8	11	9	5.16	5.59	6.92
CVE-2017-12635	10	12	7	6.06	9.11	6.01
CVE-2018-3760	28	57	64	13.78	66.37	23.33
CVE-2019-5418	26	38	45	13.19	28.41	30.68
CVE-2020-9484	19	17	13	7.39	6.78	14.73
CVE-2020-13942	77	93	63	13.06	12.28	40.39
CVE-2020-23839	28	27	36	7.44	64.32	34.79
EPS_CWE-434	398	77	245	24.97	45.1	74.71
PHP_CWE-434	64	55	57	12.29	36.41	23.95

场景	平均日志处理速度（条/ms）
场景	本算法	Drain	SwissLog
CVE-2017-7529	16.67	25.00	7.69
CVE-2017-12635	25.00	7.14	0.55
CVE-2018-3760	3.70	20.00	4.55
CVE-2019-5418	7.69	20.00	5.00
CVE-2020-9484	14.29	5.56	0.52
CVE-2020-13942	3.45	3.23	0.16
CVE-2020-23839	9.09	25.00	12.50
EPS_CWE-434	5.88	20.00	5.00
PHP_CWE-434	16.67	14.29	1.79

场景	Falco默认规则	自动生成规则	手工编写规则
CVE-2017-7529	无法检测	无法检测	无法检测
CVE-2017-12635	无法检测	v	v
CVE-2018-3760	无法检测	v	v
CVE-2019-5418	无法检测	v	v
CVE-2020-9484	v	v	v
CVE-2020-13942	无法检测	v	v
CVE-2020-23839	v	v	v
EPS_CWE-434	v	v	v
PHP_CWE-434	v	v	v