Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/2093
Title: Intelligent Enterprise Security Enhanced COPE (Intelligent ESECOPE)
Authors: Samarathunge, R. D. S. P
Perera, W. P. P
Ranasinghe, R. A. N. I
Kahaduwa, K. K. U. S
Senarathne, A. N
Abeywardena, K. Y
Keywords: Intelligent
Enterprise Security
Enhanced COPE
Intelligent ESECOPE
Issue Date: 21-Dec-2018
Publisher: IEEE
Citation: R. D. S. P. Samarathunge, W. P. P. Perera, R. A. N. I. Ranasinghe, K. K. U. S. Kahaduwa, A. N. Senarathne and K. Y. Abeywardena, "Intelligent Enterprise Security Enhanced COPE (Intelligent ESECOPE)," 2018 IEEE International Conference on Information and Automation for Sustainability (ICIAfS), 2018, pp. 1-6, doi: 10.1109/ICIAFS.2018.8913361.
Series/Report no.: 2018 IEEE International Conference on Information and Automation for Sustainability (ICIAfS);Pages 1-6
Abstract: Mobile devices have come a long way of supporting humans' day to day tasks. Companies from all over the world tend to implement Information Technology (IT) consumerization in their premises in order to attain high productivity as well as employee satisfaction. Bring Your Own Device (BYOD), Corporate Owned Personally Enabled (COPE) and Choose Your Own Device (CYOD) assist to implement IT consumerization according to the organization's requirements. This research looks at the security issues in Corporate Owned Personally Enabled concept. The purpose of this research is to identify major security concerns an organization could have and propose sophisticated yet effective countermeasures. Research components are categorized into four main parts which are web data loss prevention, email data loss prevention, malware identification and malware classification. The information leak can be occurred either deliberately or unintentionally by an individual or a group of individuals in any organization which affects financial status, customer or public security and the reputation. ESECOPE is built with a revived technique that is based on keyword-based search detection to reach the goal. Proposed Implementations consist range of features in data loss prevention such as deep content analysis, secure wiping of sensitive data, encryption of sensitive data. The combination of both machine learning techniques, signature, and behavioral based analysis will be used to craft a tool which is integrated into the system that outputs less false negative results. Apart from identification and classification generation of IT administrator alerts, Quarantine identified malware can be listed as additional features provided by the tool. The platform which supports deploying multiple vulnerability scanning tools together makes the end product unique from other existing COPE solutions provides a vast amount of advantages including mobile device scanning individually or at once, report generation and also it reduces the workload of IT administrator.
URI: http://rda.sliit.lk/handle/123456789/2093
ISSN: 2151-1810
Appears in Collections:Department of Computer Systems Engineering-Scopes
Research Papers - Dept of Computer Systems Engineering
Research Papers - IEEE
Research Papers - SLIIT Staff Publications

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