KubAnomaly: Anomaly detection for the Docker orchestration platform with neural network approaches

Kubernetes, which is the most popular orchestration platform for Docker containers, is used widely for developing microservices and automating Docker instance life cycle administration. Because of advancements in containerization technology, a single server can run multiple services and use hardware...

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Main Authors: Chin‐Wei Tien, Tse‐Yung Huang, Chia‐Wei Tien, Ting‐Chun Huang, Sy‐Yen Kuo
Format: Article
Language:English
Published: Wiley 2019-12-01
Series:Engineering Reports
Subjects:
Online Access:https://doi.org/10.1002/eng2.12080
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spelling doaj-416f4e2bba0044aa8d6e8ace6313ff962020-11-25T02:06:22ZengWileyEngineering Reports2577-81962019-12-0115n/an/a10.1002/eng2.12080KubAnomaly: Anomaly detection for the Docker orchestration platform with neural network approachesChin‐Wei Tien0Tse‐Yung Huang1Chia‐Wei Tien2Ting‐Chun Huang3Sy‐Yen Kuo4Cybersecurity Technology Institute Institute for Information Industry Taipei Taiwan, ROCCybersecurity Technology Institute Institute for Information Industry Taipei Taiwan, ROCCybersecurity Technology Institute Institute for Information Industry Taipei Taiwan, ROCCybersecurity Technology Institute Institute for Information Industry Taipei Taiwan, ROCDepartment of Electrical Engineering National Taiwan University Taipei Taiwan, ROCKubernetes, which is the most popular orchestration platform for Docker containers, is used widely for developing microservices and automating Docker instance life cycle administration. Because of advancements in containerization technology, a single server can run multiple services and use hardware resources more efficiently. However, containerized environments also bring new challenges in terms of complete monitoring and security provision. Thus, hackers can exploit the security vulnerabilities of containers to gain remote control permissions and cause extensive damage to company assets. Therefore, in this study, we propose KubAnomaly, a system that provides security monitoring capabilities for anomaly detection on the Kubernetes orchestration platform. We develop a container monitoring module for Kubernetes and implement neural network approaches to create classification models that strengthen its ability to find abnormal behaviors such as web service attacks and common vulnerabilities and exposures attacks. We use three types of datasets to evaluate our system, including privately collected and publicly available datasets as well as real‐world experiment data. Furthermore, we demonstrate the effectiveness of KubAnomaly by comparing its accuracy with that of other machine learning algorithms. KubAnomaly is shown to achieve an overall accuracy of up to 96% for anomaly detection. It successfully identifies four real attacks carried out by hackers in September 2018. Moreover, its performance overhead is only 5% greater than that of current methods. In summary, KubAnomaly significantly improves container security by avoiding anomaly attacks.https://doi.org/10.1002/eng2.12080anomaly detectioncloud securitycontainer orchestrationcontainer securitymachine learningneural network
collection DOAJ
language English
format Article
sources DOAJ
author Chin‐Wei Tien
Tse‐Yung Huang
Chia‐Wei Tien
Ting‐Chun Huang
Sy‐Yen Kuo
spellingShingle Chin‐Wei Tien
Tse‐Yung Huang
Chia‐Wei Tien
Ting‐Chun Huang
Sy‐Yen Kuo
KubAnomaly: Anomaly detection for the Docker orchestration platform with neural network approaches
Engineering Reports
anomaly detection
cloud security
container orchestration
container security
machine learning
neural network
author_facet Chin‐Wei Tien
Tse‐Yung Huang
Chia‐Wei Tien
Ting‐Chun Huang
Sy‐Yen Kuo
author_sort Chin‐Wei Tien
title KubAnomaly: Anomaly detection for the Docker orchestration platform with neural network approaches
title_short KubAnomaly: Anomaly detection for the Docker orchestration platform with neural network approaches
title_full KubAnomaly: Anomaly detection for the Docker orchestration platform with neural network approaches
title_fullStr KubAnomaly: Anomaly detection for the Docker orchestration platform with neural network approaches
title_full_unstemmed KubAnomaly: Anomaly detection for the Docker orchestration platform with neural network approaches
title_sort kubanomaly: anomaly detection for the docker orchestration platform with neural network approaches
publisher Wiley
series Engineering Reports
issn 2577-8196
publishDate 2019-12-01
description Kubernetes, which is the most popular orchestration platform for Docker containers, is used widely for developing microservices and automating Docker instance life cycle administration. Because of advancements in containerization technology, a single server can run multiple services and use hardware resources more efficiently. However, containerized environments also bring new challenges in terms of complete monitoring and security provision. Thus, hackers can exploit the security vulnerabilities of containers to gain remote control permissions and cause extensive damage to company assets. Therefore, in this study, we propose KubAnomaly, a system that provides security monitoring capabilities for anomaly detection on the Kubernetes orchestration platform. We develop a container monitoring module for Kubernetes and implement neural network approaches to create classification models that strengthen its ability to find abnormal behaviors such as web service attacks and common vulnerabilities and exposures attacks. We use three types of datasets to evaluate our system, including privately collected and publicly available datasets as well as real‐world experiment data. Furthermore, we demonstrate the effectiveness of KubAnomaly by comparing its accuracy with that of other machine learning algorithms. KubAnomaly is shown to achieve an overall accuracy of up to 96% for anomaly detection. It successfully identifies four real attacks carried out by hackers in September 2018. Moreover, its performance overhead is only 5% greater than that of current methods. In summary, KubAnomaly significantly improves container security by avoiding anomaly attacks.
topic anomaly detection
cloud security
container orchestration
container security
machine learning
neural network
url https://doi.org/10.1002/eng2.12080
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