International Journal of Knowledge Based Computer System

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

To Study the Hypervisor Scanner Model with ANN for Cloud Systems


Affiliations
1 Research Scholar, Computer Application Department, Himalayan Garhwal University, Pauri Garhwal, Uttarakhand, India
2 Faculty of Computer Application, Himalayan Garhwal University, Pauri Garhwal, Uttarakhand, India
3 Assistant Professor, Department of Physics, Dr. PDBH Government PG College, Kotdwar, Uttarakhand, India
     

   Subscribe/Renew Journal


The detection part is called the Hypervisor Scanner, which is programmed to detect malicious insiders. Using the feed forward neural network, the hypervisor scanner is generated and trained with a supervised learning algorithm referred to as the Levenberg-Marquardt algorithm. The three criteria are considered for service level agreement, such as bandwidth requirement, memory consumption and storage space. The Hypervisor Scanner can detect malicious insiders in cloud systems that breach SLA and suffer from an insecure cloud administrative domain that lacks control over the cloud service provider (CSP). Here, the Hypervisor Scanner is constructed using the biologically inspired classification approach referred to as artificial neural network modelling. ANN teaching uses the Levenberg-Marquardt learning algorithm. The LM algorithm works by minimising the average square error to boost the detection system. For detecting malicious insiders, the Hypervisor Scanner uses threshold values for SLA parameters. It is known from performance review and comparison that the Hypervisor Scanner is the appropriate one with high detection accuracy and low false alarm rate for the detection of malicious insiders. This can therefore be effective, robust and realistic in the detection of malicious insiders in and around the cloud world.

Keywords

Architecture, Cloud Computing, Commercial and Technological, Hypervisor Scanner
Subscription Login to verify subscription
User
Notifications
Font Size


  • D. Catteddu, and G. Hogben, “Cloud computing: Benefits, risks and recommendations for information security,” European Network and Information Security Agency, Technical Report, 2009.

  • Bruening and Treacy, “Cloud computing: Privacy, security challenges,” Bureau of National Affairs, 2009.

  • H. Takabi, J. B. D. Joshi, and G.-J. Ahn, “Security and privacy challenges in cloud computing environments,” IEEE Security & Privacy, vol. 8, no. 6, pp. 24-31, Nov.Dec. 2010, doi: 10.1109/MSP.2010.186.

  • Y. Chen, V. Paxson, and R. H. Katz, “What’s new about cloud computing security,” University of California, Berkeley, Report No. UCB/EECS-2010-5, Jan. 20, 2010.

  • A. P. Nirmala, and R. Sridaran, “Cloud computing issues at design and implementation levels - A survey,” International Journal of Advanced Networking and Applications, vol. 3, no. 6, p. 1444, 2012.

  • A. M. Bento, and A. K. Aggarwal, Cloud Computing Service and Deployment Models: Layers and Management. USA: IGI Global, 2012, pp. 1-365.

  • H. Jin, F. Xu, F. Liu, L. Liu, B. Li, and B. Li, “iAware: Making live migration of virtual machines interference Aware in the cloud,” IEEE Transactions on Computers, vol. 63, no. 12, pp. 3012-3025, 2014.

  • V. Vinothina, R. Sridaran, and P. Ganapathi, “A survey on resource allocation strategies in cloud computing,” International Journal of Advanced Computer Science and Applications, vol. 3, no. 6, pp. 97-104, 2012.

  • F. Sabahi, “Secure virtualization for cloud environment using hypervisor-based technology,” International Journal of Machine Learning and Computing, vol. 2, no. 1, pp. 39-45, 2012.

  • M. Maurer, D. Borgetto, G. Da-Costa, J.-M. Pierson, and I. Brandic, “Energy-efficient and SLA-aware management of IaaS clouds,” in 2012 Third International Conference on Future Systems: Where Energy, Computing and Communication Meet (E-Energy), IEEE, May 2012, pp. 1-10.

  • M. Abdel-Azim, A. I. Abdel-Fatah, and M. Awad, “Performance analysis of artificial neural network intrusion detection systems,” in 2009 International Conference on Electrical and Electronics Engineering, ELECO 2009, IEEE, 2009, pp. II-385.

  • O. Linda, T. Vollmer, and M. Manic, “Neural network based intrusion detection system for critical infrastructures,” in 2009 International Joint Conference on Neural Networks, IEEE, 2009, pp. 1827-1834.

  • H. Yu, and B. M. Wilamowski, “Efficient and reliable training of neural networks,” in 2009 2nd Conference on Human System Interactions, IEEE, May 2009, pp. 109-115.

  • J. Pradeep, E. Srinivasan, and S. Himavathi, “Diagonal based feature extraction for handwritten character recognition system using neural network,” in 2011 3rd International Conference on Electronics Computer Technology, IEEE, 2011, vol. 4, pp. 364-368.

  • P. J. Yang, and C. C. Fung, “Artificial intelligence in malware - Cop or culprit?,” 2008.

  • Weiwei, and Donghai, “Cascade disturbance rejection control of the uncertain nonlinear systems with

  • nonlinear parameterization,” in 2013 American Control Conference, IEEE, 2013, pp. 265-271.


Abstract Views: 311

PDF Views: 0




  • To Study the Hypervisor Scanner Model with ANN for Cloud Systems

Abstract Views: 311  |  PDF Views: 0

Authors

Anshu Mali Bhushan
Research Scholar, Computer Application Department, Himalayan Garhwal University, Pauri Garhwal, Uttarakhand, India
Harsh Kumar
Faculty of Computer Application, Himalayan Garhwal University, Pauri Garhwal, Uttarakhand, India
Devendra Singh Chauhan
Assistant Professor, Department of Physics, Dr. PDBH Government PG College, Kotdwar, Uttarakhand, India

Abstract


The detection part is called the Hypervisor Scanner, which is programmed to detect malicious insiders. Using the feed forward neural network, the hypervisor scanner is generated and trained with a supervised learning algorithm referred to as the Levenberg-Marquardt algorithm. The three criteria are considered for service level agreement, such as bandwidth requirement, memory consumption and storage space. The Hypervisor Scanner can detect malicious insiders in cloud systems that breach SLA and suffer from an insecure cloud administrative domain that lacks control over the cloud service provider (CSP). Here, the Hypervisor Scanner is constructed using the biologically inspired classification approach referred to as artificial neural network modelling. ANN teaching uses the Levenberg-Marquardt learning algorithm. The LM algorithm works by minimising the average square error to boost the detection system. For detecting malicious insiders, the Hypervisor Scanner uses threshold values for SLA parameters. It is known from performance review and comparison that the Hypervisor Scanner is the appropriate one with high detection accuracy and low false alarm rate for the detection of malicious insiders. This can therefore be effective, robust and realistic in the detection of malicious insiders in and around the cloud world.

Keywords


Architecture, Cloud Computing, Commercial and Technological, Hypervisor Scanner

References