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

Cloud Security Framework:VM Features Based Intrusion Detection System


Affiliations
1 Department of Computer Science, I.L.V.A. Commerce & Science College, Indore, Madhya Pradesh, India
2 Computer Center, D.A.V.V, Indore, Madhya Pradesh, India
     

   Subscribe/Renew Journal


Cloud services provide resources that are accessed remotely over the network. The distributed architecture of Cloud deployed over the internet, exposes it to several network attacks. To provide a secure architecture, several frameworks using various security tools have been proposed. However, due to the dynamic nature of cloud infrastructure, newer challenges have to be addressed. Here, we propose a Multilevel Intrusion detection system using virtual machine’s feature and behaviour to provide a secure Cloud architecture. This framework deploys VM’s feature based signature intrusion detection system on each VM (instance) in the cloud. IDS are configured for every VM at the time of its launch according to the features defined by the user and updated thereafter according to the traffic pattern at that VM, by a Control unit at the host level. The framework developed works at optimal computational cost, minimum packet drop and acceptable attack detection rate. For verifying the functional validation and effectiveness of this framework, we have developed a prototype considering few known attacks signatures.

Keywords

Cloud Computing, Network Based Intrusion Detection System, Snort, Virtual Machine.
Subscription Login to verify subscription
User
Notifications
Font Size


  • International Data Corporation. Retrieved from http://blogs.idc.com/ie/wp-content/uploads/2009/12/idc_cloud_challenges_2009.
  • F. Gens, IT Cloud services user survey, top benefits and challenges, International Data Corporation.
  • C. Modi, D. Patel, B. Borisaniya, H. Patel, A. Patel, and R. Muttukrishnan, “A survey on security issues and solutions at different layers of cloud computing,” The Journal of Supercomputing, vol. 63, no. 2, pp. 561-592, 2013.
  • L. Martin, “Awareness, trust and security to shape government cloud adoption,” White Paper, 2010.
  • C. C. Lo, C. C. Huang, and J. Ku, “A cooperative intrusion detection system framework for cloud computing networks,” in Proceedings of the 39th International Conference on Parallel Processing Workshops (ICPPW), pp. 280-284, 2010.
  • C. Mazzariello, R. Bifulco, and R. Canonico, “Integrating a network IDS into an open source cloud computing environment,” in Proceedings of the Sixth International Conference on Information Assurance and Security, pp. 265-270, 2010.
  • A. Bakshi, and B. Yogesh, “Securing cloud from DDOS attacks using intrusion detection system in virtual machine,” in Proceedings of the Second International Conference on Communication Software and Networks, pp. 260-264, 2010.
  • I. Gul, and M. Hussain, “Distributed cloud intrusion detection model,” in International Journal of Advanced Science and Technology, pp. 71-82, 2011.
  • D. Smallwood, and A. Vance, “Intrusion analysis with deep packet inspection: Increasing efficiency of packet based investigations,” in Proceedings of the International Conference on Cloud and Service Computing, pp. 342-347, 2011.
  • C. N. Modi, and D. Patel, “A novel Hybrid-Network Intrusion Detection System (H-NIDS) in cloud computing,” 2013 IEEE Symposium on Computational Intelligence in Cyber Security (CICS), Singapore, pp. 23-30, 2013.
  • S. Gupta, P. Kumar, and A. Abraham, “A profile based network intrusion detection and prevention system for securing cloud environment,” International Journal of Distribute Sensor Networks, pp. 1-12, 2013.
  • L. Schaelicke, T. Slabach, B. Moore, and C. Freeland, “Characterizing the performance of network intrusion detection sensors,” in RAID 2003, LNCS 2820, pp. 155-172, 2003.
  • Snort Tool, Snort-home page. Retrieved from https://www.snort.org/, May 28, 2018.
  • Hping security tool. Retrieved from http://www. hping. Org/, June 10, 2018.
  • Nmap. Retrieved from http://nmap.org/, June 10, 2018.
  • Y. Tayyebi, and D. S. Bhilare, “Cloud security through Intrusion Detection System (IDS): Review of existing solutions,” International Journal of Emerging Trends & Technology in Computer Science, vol. 4, no. 6, pp. 213-215, 2015.

Abstract Views: 413

PDF Views: 0




  • Cloud Security Framework:VM Features Based Intrusion Detection System

Abstract Views: 413  |  PDF Views: 0

Authors

Yakuta Tayyebi
Department of Computer Science, I.L.V.A. Commerce & Science College, Indore, Madhya Pradesh, India
D. S. Bhilare
Computer Center, D.A.V.V, Indore, Madhya Pradesh, India

Abstract


Cloud services provide resources that are accessed remotely over the network. The distributed architecture of Cloud deployed over the internet, exposes it to several network attacks. To provide a secure architecture, several frameworks using various security tools have been proposed. However, due to the dynamic nature of cloud infrastructure, newer challenges have to be addressed. Here, we propose a Multilevel Intrusion detection system using virtual machine’s feature and behaviour to provide a secure Cloud architecture. This framework deploys VM’s feature based signature intrusion detection system on each VM (instance) in the cloud. IDS are configured for every VM at the time of its launch according to the features defined by the user and updated thereafter according to the traffic pattern at that VM, by a Control unit at the host level. The framework developed works at optimal computational cost, minimum packet drop and acceptable attack detection rate. For verifying the functional validation and effectiveness of this framework, we have developed a prototype considering few known attacks signatures.

Keywords


Cloud Computing, Network Based Intrusion Detection System, Snort, Virtual Machine.

References