Abstract
Security is one of the major concerns in cloud computing now-a-days. Malicious code deployment is the main cause of threat in today’s cloud paradigm. Antivirus software unable to detect many modern malware threats which causes serious impacts in basic cloud operations. This paper counsels a new model for malware detection on cloud architecture. This model enables identification of malicious and unwanted software by amalgamation of multiple detection engines. This paper follows DNA sequence detection process, symbolic detection process, and behavioural detection process to detect various threats. The proposed approach (PMDM) can be deployed on a VMM which remains fully transparent to guest VM and to cloud users. However, PMDM prevents the malicious code running in one VM (infected VM) to spread into another noninfected VM with help of hosted VMM. After detecting malicious code by PMDM technique, it warns the other guest VMs about it. In this paper, a prototype of PMDM is partially implemented on one popular open-source cloud architecture—Eucalyptus.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Marinescu D.C., “Cloud Computing: Theory and Practice”, MK Publication (2013).
Dahl G.E., Stokes J.W. et al., “Large-scale malware classification using random projections and neural networks”, IEEE International Conference
Hatem S.S., wafy M.H., et al., “Malware Detection in Cloud Computing”, International Journal of Advanced Computer Science and Applications (IJACSA), Vol 5, Science and Information (2014).
Graham M., “Behaviour of Botnets and Other Malware in Virtual Environments”, The Open Web Application Security Project (2014).
Oberheide J., Cooke E. et al., “CloudAV: N-Version Antivirus in the Network Cloud”, 17th conference on Security symposium, pp- 91–106 (2008).
Pedersen j., Bastola D., et al., “BLAST Your Way through Malware Malware Analysis Assisted by Bioinformatics Tools”, International Conference on Security and Management (2012).
Mandl T., Bayer U.et al., “ANUBIS ANalyzing Unknown BInarieS The automatic Way”, VIRUS Bulletin Conference, v 1.0.02 (2009).
Johnson D, Murari K. et al., “Eucalyptus Beginner’s Guide- UEC Edition”, v1.0 (2010).
Parmar H., Champaneria T., “Comparative Study of Open Nebula, Eucalyptus, Open Stack and Cloud Stack”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol.4, No. 2, pp 714–721 (2014).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Shaw, S., Gupta, M.K., Chakraborty, S. (2017). Cloud Based Malware Detection Technique. In: Satapathy, S., Bhateja, V., Udgata, S., Pattnaik, P. (eds) Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications . Advances in Intelligent Systems and Computing, vol 515. Springer, Singapore. https://doi.org/10.1007/978-981-10-3153-3_48
Download citation
DOI: https://doi.org/10.1007/978-981-10-3153-3_48
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3152-6
Online ISBN: 978-981-10-3153-3
eBook Packages: EngineeringEngineering (R0)