Detection of Malware Attacks on Virtual Machines for a Self-Heal Approach in Cloud Computing using VM Snapshots
Cloud Computing strives to be dynamic as a service oriented architecture. The services in the SoA are rendered in terms of private, public and in many other commercial domain aspects. These services should be secured and thus are very vital to the cloud infrastructure. In order, to secure and mainta...
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doaj-cf4d4370887d4cc6a56862d75115f83b2020-11-24T22:03:04ZengCroatian Communications and Information Society (CCIS)Journal of Communications Software and Systems1845-64211846-60792018-09-01143249257Detection of Malware Attacks on Virtual Machines for a Self-Heal Approach in Cloud Computing using VM SnapshotsLinda JosephRajeswari MukeshCloud Computing strives to be dynamic as a service oriented architecture. The services in the SoA are rendered in terms of private, public and in many other commercial domain aspects. These services should be secured and thus are very vital to the cloud infrastructure. In order, to secure and maintain resilience in the cloud, it not only has to have the ability to identify the known threats but also to new challenges that target the infrastructure of a cloud. In this paper, we introduce and discuss a detection method of malwares from the VM logs and corresponding VM snapshots are classified into attacked and non-attacked VM snapshots. As snapshots are always taken to be a backup in the backup servers, especially during the night hours, this approach could reduce the overhead of the backup server with a self-healing capability of the VMs in the local cloud infrastructure. A machine learning approach at the hypervisor level is projected, the features being gathered from the API calls of VM instances in the IaaS level of cloud service. Our proposed scheme can have a high detection accuracy of about 93% while having the capability to classify and detect different types of malwares with respect to the VM snapshots. Finally the paper exhibits an algorithm using snapshots to detect and thus to self-heal using the monitoring components of a particular VM instances applied to cloud scenarios. The self-healing approach with machine learning algorithms can determine new threats with some prior knowledge of its functionality. https://jcomss.fesb.unist.hr/index.php/jcomss/article/view/537Cloud ComputingVM SnapshotsAPI CallsIaaSSelf-HealingMachine Learning Algorithms |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Linda Joseph Rajeswari Mukesh |
spellingShingle |
Linda Joseph Rajeswari Mukesh Detection of Malware Attacks on Virtual Machines for a Self-Heal Approach in Cloud Computing using VM Snapshots Journal of Communications Software and Systems Cloud Computing VM Snapshots API Calls IaaS Self-Healing Machine Learning Algorithms |
author_facet |
Linda Joseph Rajeswari Mukesh |
author_sort |
Linda Joseph |
title |
Detection of Malware Attacks on Virtual Machines for a Self-Heal Approach in Cloud Computing using VM Snapshots |
title_short |
Detection of Malware Attacks on Virtual Machines for a Self-Heal Approach in Cloud Computing using VM Snapshots |
title_full |
Detection of Malware Attacks on Virtual Machines for a Self-Heal Approach in Cloud Computing using VM Snapshots |
title_fullStr |
Detection of Malware Attacks on Virtual Machines for a Self-Heal Approach in Cloud Computing using VM Snapshots |
title_full_unstemmed |
Detection of Malware Attacks on Virtual Machines for a Self-Heal Approach in Cloud Computing using VM Snapshots |
title_sort |
detection of malware attacks on virtual machines for a self-heal approach in cloud computing using vm snapshots |
publisher |
Croatian Communications and Information Society (CCIS) |
series |
Journal of Communications Software and Systems |
issn |
1845-6421 1846-6079 |
publishDate |
2018-09-01 |
description |
Cloud Computing strives to be dynamic as a service oriented architecture. The services in the SoA are rendered in terms of private, public and in many other commercial domain aspects. These services should be secured and thus are very vital to the cloud infrastructure. In order, to secure and maintain resilience in the cloud, it not only has to have the ability to identify the known threats but also to new challenges that target the infrastructure of a cloud. In this paper, we introduce and discuss a detection method of malwares from the VM logs and corresponding VM snapshots are classified into attacked and non-attacked VM snapshots. As snapshots are always taken to be a backup in the backup servers, especially during the night hours, this approach could reduce the overhead of the backup server with a self-healing capability of the VMs in the local cloud infrastructure. A machine learning approach at the hypervisor level is projected, the features being gathered from the API calls of VM instances in the IaaS level of cloud service. Our proposed scheme can have a high detection accuracy of about 93% while having the capability to classify and detect different types of malwares with respect to the VM snapshots. Finally the paper exhibits an algorithm using snapshots to detect and thus to self-heal using the monitoring components of a particular VM instances applied to cloud scenarios. The self-healing approach with machine learning algorithms can determine new threats with some prior knowledge of its functionality. |
topic |
Cloud Computing VM Snapshots API Calls IaaS Self-Healing Machine Learning Algorithms |
url |
https://jcomss.fesb.unist.hr/index.php/jcomss/article/view/537 |
work_keys_str_mv |
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