Comparison of back propagation algorithm and SVM on SLA based masquerader detection in cloud
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Department of Computer Science and Information Technology
Abstract
Cloud computing is a prospering technology that most organizations are considering for
adoption as a cost effective strategy for managing IT. However, organizations still consider
the technology to be associated with many business risks that are yet to be resolved. Such
issues include security, privacy as well as legal and regulatory risks. As an initiative to
address such risks, organizations can develop and implement Service Level Agreement
(SLA) to establish common expectations and goals between the cloud provider and customer.
Organizations can base on the SLA to address the security concern. However, many SLAs
tend to focus on cloud computing performance whilst neglecting information security issues.
This study is oriented to build a masquerade detection system in cloud computing, based on
the proposed SLA.
The new SLA contains additional security constraints than that found in traditional SLA such
as length of temporal sequence, weight of each activities and the threshold weight of the
temporal sequence.
The performance analysis includes comparison of BackPropagation algorithm with SVM.
The detection rate and false alarm rate is observed and found that it can detect masqueraders
well from the small set of training data with small false alarm rate.
Keywords: Cloud Computing, Service Level Agreement, Masquerader, Backpropagation
Algorithm, Support Vector Machine, Temporal Sequence