Virtual machine anomaly detection strategy based on cloud platform operating environment perception

2018 ◽  
Vol 30 (22) ◽  
pp. e4656 ◽  
Author(s):  
Xuna Miao ◽  
Xiaobo Wu
Keyword(s):  
Anomaly Detection ◽  
Virtual Machine ◽  
Cloud Platform ◽  
Operating Environment ◽  
Detection Strategy ◽  
Environment Perception

scholarly journals An Anomaly Detection Algorithm of Cloud Platform Based on Self-Organizing Maps

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Jun Liu ◽  
Shuyu Chen ◽  
Zhen Zhou ◽  
Tianshu Wu
Keyword(s):  
Anomaly Detection ◽  
Virtual Machine ◽  
Large Scale ◽  
Virtual Machines ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Cloud Platform ◽  
Self Organizing Maps ◽  
Detection Speed ◽  
Self Organizing

Virtual machines (VM) on a Cloud platform can be influenced by a variety of factors which can lead to decreased performance and downtime, affecting the reliability of the Cloud platform. Traditional anomaly detection algorithms and strategies for Cloud platforms have some flaws in their accuracy of detection, detection speed, and adaptability. In this paper, a dynamic and adaptive anomaly detection algorithm based on Self-Organizing Maps (SOM) for virtual machines is proposed. A unified modeling method based on SOM to detect the machine performance within the detection region is presented, which avoids the cost of modeling a single virtual machine and enhances the detection speed and reliability of large-scale virtual machines in Cloud platform. The important parameters that affect the modeling speed are optimized in the SOM process to significantly improve the accuracy of the SOM modeling and therefore the anomaly detection accuracy of the virtual machine.

A Defense Mechanism Based on Improved Allocation Strategy of VMs Co-Resident Attack in Power Cloud Platform

2020 ◽  
Vol 13 ◽  
Author(s):  
Yuancheng Li ◽  
Pan Zhang ◽  
Daoxing Li ◽  
Jing Zeng
Keyword(s):  
Virtual Machine ◽  
Simulation Experiment ◽  
Virtual Machines ◽  
Defense Mechanism ◽  
Security Threats ◽  
Allocation Strategy ◽  
Cloud Platform ◽  
Deployment Strategy ◽  
Dbscan Algorithm ◽  
Maximum Minimum

Background: Cloud platform is widely used in electric power field. Virtual machine co-resident attack is one of the major security threats to the existing power cloud platform. Objective: This paper proposes a mechanism to defend virtual machine co-resident attack on power cloud platform. Method: Our defense mechanism uses the DBSCAN algorithm to classify and output the classification results through the random forest and uses improved virtual machine deployment strategy which combines the advantages of random round robin strategy and maximum/minimum resource strategy to deploy virtual machines. Results: we made a simulation experiment on power cloud platform of State Grid and verified the effectiveness of proposed defense deployment strategy. Conclusion: After the virtual machine deployment strategy is improved, the coverage of the virtual machine is remarkably reduced which proves that our defense mechanism achieves some effect of defending the virtual machine from virtual machine co-resident attack.

Improved t-SNE in Anomaly Detection of Cloud Virtual Machine

2021 ◽  
pp. 1-26
Author(s):  
Fu Zhuang ◽  
Guoyuan Lin ◽  
Huanye He ◽  
Yifan Zhang ◽  
Yonggang Li ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Virtual Machine

Design on Framework for Dynamic Monitoring of Virtual Machines

2014 ◽  
Vol 530-531 ◽  
pp. 667-670
Author(s):  
Ke Ming Chen
Keyword(s):  
Operating System ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Dynamic Monitoring ◽  
Cloud Platform ◽  
Monitoring Framework ◽  
New Framework

In order to ensure that the cloud platform client runtime kernel virtual machine security, this paper proposes a new framework for dynamic monitoring of virtual machines, it is for the kernel rootkit attacks, study the cloud client virtual machine operating system kernel safety, presented Hyperchk virtual machine dynamic monitoring framework. This framework mainly for kernel rootkit attacks, ensure that customers running virtual machine kernel security.

scholarly journals Research on New Virtualization Security Protection Management System Based on Cloud Platform

2022 ◽  
Vol 2146 (1) ◽  
pp. 012010
Author(s):  
Zhihong Li ◽  
Guangxu Liu ◽  
Yijie Dang ◽  
Zhijie Shang ◽  
Nan Lin
Keyword(s):  
Business Model ◽  
Virtual Machine ◽  
Management System ◽  
Security Threats ◽  
Cloud Platform ◽  
Virtual Machine Migration ◽  
It Resources ◽  
The Stability ◽  
Machine Communication ◽  
New Situation

Abstract As an emerging product under the condition of informatization, the utilization of cloud platform in many industries has brought fundamental changes to the production and business model in related fields. The cloud platform provides rich and diverse utilization services to terminals through multi-dimensional integration of different IT resources. With the in-depth utilization of cloud platform, the security problems it faces are becoming more and more prominent. The traditional network security protection means have been difficult to effectively adapt to and deal with the security threats under the new situation of cloud platform utilization. As a prominent part of building cloud platform, the construction level of virtualization security protection system will have an intuitive impact on the security of cloud platform. At present, the virtualization security protection management system under cloud platform is facing direct threats from virtual machine deployment, virtual machine communication and virtual machine migration. Based on this, this paper studies the virtualization security protection management system of cloud platform from the perspective of virtualization security tech, so as to ameliorate the stability, reliability and security of cloud platform.

Optimal virtual machine selection for anomaly detection using a swarm intelligence approach

2019 ◽  
Vol 84 ◽  
pp. 105686 ◽  
Author(s):  
Aravinthkumar Selvaraj ◽  
Rizwan Patan ◽  
Amir H. Gandomi ◽  
Ganesh Gopal Deverajan ◽  
Manjula Pushparaj
Keyword(s):  
Anomaly Detection ◽  
Swarm Intelligence ◽  
Virtual Machine ◽  
Machine Selection ◽  
Selection For ◽  
Intelligence Approach

scholarly journals An Incongruence-Based Anomaly Detection Strategy for Analyzing Water Pollution in Images from Remote Sensing

2019 ◽  
Vol 12 (1) ◽  
pp. 43 ◽  
Author(s):  
Maurício Araújo Dias ◽  
Erivaldo Antônio da Silva ◽  
Samara Calçado de Azevedo ◽  
Wallace Casaca ◽  
Thiago Statella ◽  
...  
Keyword(s):  
Water Pollution ◽  
Remote Sensing ◽  
Anomaly Detection ◽  
Synthetic Data ◽  
Open Water ◽  
Surveillance Systems ◽  
Environmental Disasters ◽  
Detection Strategy ◽  
Potential Applications ◽  
Static Images

The potential applications of computational tools, such as anomaly detection and incongruence, for analyzing data attract much attention from the scientific research community. However, there remains a need for more studies to determine how anomaly detection and incongruence applied to analyze data of static images from remote sensing will assist in detecting water pollution. In this study, an incongruence-based anomaly detection strategy for analyzing water pollution in images from remote sensing is presented. Our strategy semi-automatically detects occurrences of one type of anomaly based on the divergence between two image classifications (contextual and non-contextual). The results indicate that our strategy accurately analyzes the majority of images. Incongruence as a strategy for detecting anomalies in real-application (non-synthetic) data found in images from remote sensing is relevant for recognizing crude oil close to open water bodies or water pollution caused by the presence of brown mud in large rivers. It can also assist surveillance systems by detecting environmental disasters or performing mappings.

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