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Izvorni znanstveni članak
https://doi.org/10.24138/jcomss.v14i3.537

Detection of Malware Attacks on Virtual Machines for a Self-Heal Approach in Cloud Computing using VM Snapshots

Linda Joseph   ORCID icon orcid.org/0000-0003-1673-6410 ; Hindustan Institute of Technology and Science, Chennai, Tamil Nadu, India
Rajeswari Mukesh ; Hindustan Institute of Technology and Science, Chennai, Tamil Nadu, India

Puni tekst: engleski, pdf (2 MB) str. 249-257 preuzimanja: 243* citiraj
APA 6th Edition
Joseph, L. i Mukesh, R. (2018). Detection of Malware Attacks on Virtual Machines for a Self-Heal Approach in Cloud Computing using VM Snapshots. Journal of Communications Software and Systems, 14 (3), 249-257. https://doi.org/10.24138/jcomss.v14i3.537
MLA 8th Edition
Joseph, Linda i 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, vol. 14, br. 3, 2018, str. 249-257. https://doi.org/10.24138/jcomss.v14i3.537. Citirano 22.11.2019.
Chicago 17th Edition
Joseph, Linda i 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 14, br. 3 (2018): 249-257. https://doi.org/10.24138/jcomss.v14i3.537
Harvard
Joseph, L., i Mukesh, R. (2018). 'Detection of Malware Attacks on Virtual Machines for a Self-Heal Approach in Cloud Computing using VM Snapshots', Journal of Communications Software and Systems, 14(3), str. 249-257. https://doi.org/10.24138/jcomss.v14i3.537
Vancouver
Joseph L, Mukesh R. Detection of Malware Attacks on Virtual Machines for a Self-Heal Approach in Cloud Computing using VM Snapshots. Journal of Communications Software and Systems [Internet]. 2018 [pristupljeno 22.11.2019.];14(3):249-257. https://doi.org/10.24138/jcomss.v14i3.537
IEEE
L. Joseph i R. 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, vol.14, br. 3, str. 249-257, 2018. [Online]. https://doi.org/10.24138/jcomss.v14i3.537

Sažetak
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.

Ključne riječi
Cloud Computing; VM Snapshots; API Calls; IaaS; Self-Healing; Machine Learning Algorithms

Hrčak ID: 205758

URI
https://hrcak.srce.hr/205758

Posjeta: 317 *