A Hidden Markov Model-Based Method for Virtual Machine Anomaly Detection

被引:0
作者
Shi, Chaochen [1 ]
Yu, Jiangshan [2 ]
机构
[1] China Mobile IoT Ltd, Chongqing, Peoples R China
[2] Monash Univ, Melbourne, Vic, Australia
来源
PROVABLE SECURITY, PROVSEC 2019 | 2019年 / 11821卷
关键词
Hidden Markov Model; Virtual machine; Anomaly detection; Cloud computing;
D O I
10.1007/978-3-030-31919-9_24
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The normal operation of virtual machine is a necessity for supporting cloud service. Motivated by the great desire of automated abmornal operation detection, this paper proposes a Hidden Markov Model-based method to conduct anomaly detection of virtual machine. This model can depict normal outline base of virtual machine operation and detect system outliers through calculating non-match rate. Through verifying the method in a real distributed environment, experiment results indicate that this method has 1.1%-4.9% better detection accuracy compared with two leading benchmarks with a much better efficiency.
引用
收藏
页码:372 / 380
页数:9
相关论文
共 50 条
[31]   A hidden Markov model-based assembly contact recognition system [J].
Lau, HYK .
MECHATRONICS, 2003, 13 (8-9) :1001-1023
[32]   Hidden Markov model-based approach for multimode process monitoring [J].
Wang, Fan ;
Tan, Shuai ;
Shi, Hongbo .
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2015, 148 :51-59
[33]   Hidden Markov Model-based Warm-start of Active Set Method in Model Predictive Control [J].
Kohut, Roman ;
Galciova, Lenka ;
Fedorova, Kristina ;
Abelova, Tereza ;
Bakosova, Monika ;
Kvasnica, Michal .
PROCESS CONTROL '21 - PROCEEDING OF THE 2021 23RD INTERNATIONAL CONFERENCE ON PROCESS CONTROL (PC), 2021, :60-65
[34]   Semi-Markov Switching Vector Autoregressive Model-Based Anomaly Detection in Aviation Systems [J].
Melnyk, Igor ;
Banerjee, Arindam ;
Matthews, Bryan ;
Oza, Nikunj .
KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2016, :1065-1074
[35]   An unsupervised anomaly detection approach using subtractive clustering and Hidden Markov Model [J].
Yang, Chun ;
Deng, Feiqi ;
Yang, Haidong .
2007 SECOND INTERNATIONAL CONFERENCE IN COMMUNICATIONS AND NETWORKING IN CHINA, VOLS 1 AND 2, 2007, :123-126
[36]   A graph model-based multiscale feature fitting method for unsupervised anomaly detection [J].
Zhang, Fanghui ;
Kan, Shichao ;
Zhang, Damin ;
Cen, Yigang ;
Zhang, Linna ;
Mladenovic, Vladimir .
PATTERN RECOGNITION, 2023, 138
[37]   Anomalous Behavior Detection of Marine Vessels Based on Hidden Markov Model [J].
Toloue, Kamran Fartash ;
Jahan, Majid Vafaei .
2018 6TH IRANIAN JOINT CONGRESS ON FUZZY AND INTELLIGENT SYSTEMS (CFIS), 2018, :10-12
[38]   Combining Hidden Markov Models for Improved Anomaly Detection [J].
Khreich, Wael ;
Granger, Eric ;
Sabourin, Robert ;
Miri, Ali .
2009 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS, VOLS 1-8, 2009, :965-+
[39]   Improved t-SNE in Anomaly Detection of Cloud Virtual Machine [J].
Zhuang, Fu ;
Lin, Guoyuan ;
He, Huanye ;
Zhang, Yifan ;
Li, Yonggang ;
Gu, Hao .
ENTERPRISE INFORMATION SYSTEMS, 2023, 17 (03)
[40]   Apnea Detection Based on Hidden Markov Model Kernel [J].
Travieso, Carlos M. ;
Alonso, Jesus B. ;
Ticay-Rivas, Jaime R. ;
del Pozo-Banos, Marcos .
ADVANCES IN NONLINEAR SPEECH PROCESSING, 2011, 7015 :71-79