Hypervisor-based cloud intrusion detection through online multivariate statistical change tracking

被引：41
|
作者：
Aldribi, Abdulaziz [1 ]
Traore, Issa [2 ]
Moa, Belaid [2 ]
Nwamuo, Onyekachi [2 ]
机构：
[1] Qassim Univ, Dept Comp Engn, Buraydah, Saudi Arabia
[2] Univ Victoria, Dept Elect & Comp Engn, Victoria, BC, Canada
来源：
COMPUTERS & SECURITY | 2020年 / 88卷
关键词：
Cloud computing; Cloud security monitoring; Hypervisor-based intrusion detection; Anomaly detection; Change detection; Multistage attacks; R-PACKAGE; ATTACKS; SYSTEM;
D O I：
10.1016/j.cose.2019.101646
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Cloud computing is facing a multidimensional and rapidly evolving threat landscape, making intrusion detection more challenging. This paper introduces a new hypervisor-based cloud intrusion detection system (IDS) that uses online multivariate statistical change analysis to detect anomalous network behaviors. As a departure from the conventional monolithic network IDS feature model, we leverage the fact that a hypervisor consists of a collection of instances, to introduce an instance-oriented feature model that exploits the individual and correlated behaviors of instances to improve the detection capability. The proposed approach is evaluated by collecting and using a new cloud intrusion dataset that includes a wide variety of attack vectors. (C) 2019 Elsevier Ltd. All rights reserved.
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页数：21
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