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eCloudIDS Tier-1 uX-Engine Subsystem Design and Implementation Using Self-Organizing Map (SOM) for Secure Cloud Computing Environment

  • Conference paper
Recent Trends in Computer Networks and Distributed Systems Security (SNDS 2012)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 335))

Abstract

Cloud computing is becoming more influential as a technical-cum-business model in the present scenario of enterprise business computing. It attracts the customers with its glossy catchphrase ‘pay-as-you-use’. Even after knowing all its benefits, many organizations ranging from medium to large businesses fear migrating to this computing paradigm because of the security issues associated with it. The reason being, today’s business computing world breathes solely on users and their data which require sophisticated mechanisms to protect it against theft and misuse. Subsequently, due to the public and multi-tenancy nature of cloud, the security threats and the velocity of consequences are higher in cloud, than in in-premises computing. eCloudIDS a next-generation security system designed with innovative hybrid two-tier expert engines, namely uX-Engine (tier-1) and sX-Engine (tier-2), is considered as a most suitable security solution for cloud computing environments; precisely public cloud. This paper deals with the design and implementation of our proposed eCloudIDS architecture’s Tier-1 uX-Engine Subsystem using one of the unsupervised machine learning techniques named Self-Organizing Map (SOM). This experiment was conducted on the setup with 6 machines which had Ubuntu 10.04 LTS 64-bit LTS Desktop edition as native operating system, CloudStack 3.0.0 as IaaS platform, XenServer 6.0 as virtualization host, and all systems with statically allocated IP addresses. This paper travels through the phases and footprints involved in the implementation of proposed eCloudIDS Tier-1 uX-Engine subsystem architecture using SOM. Further, our implemented system showcases the detection performance rate as 89% with minimal false alarm rates, which is considerably substantial for an unsupervised machine learning implementation.

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Srinivasan, M.K., Sarukesi, K., Keshava, A., Revathy, P. (2012). eCloudIDS Tier-1 uX-Engine Subsystem Design and Implementation Using Self-Organizing Map (SOM) for Secure Cloud Computing Environment. In: Thampi, S.M., Zomaya, A.Y., Strufe, T., Alcaraz Calero, J.M., Thomas, T. (eds) Recent Trends in Computer Networks and Distributed Systems Security. SNDS 2012. Communications in Computer and Information Science, vol 335. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34135-9_42

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  • DOI: https://doi.org/10.1007/978-3-642-34135-9_42

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34134-2

  • Online ISBN: 978-3-642-34135-9

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