Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(425.8KB)
This is an Open Access publication published under CSC-OpenAccess Policy.
The Royal Split Paradigm: Real-Time Data Fragmentation and Distributed Networks for Data Loss Prevention
Jacob Matthew Hadden, Ahmed M. Mahdy
Pages - 107 - 119     |    Revised - 31-07-2016     |    Published - 31-08-2016
Volume - 10   Issue - 3    |    Publication Date - August 2016  Table of Contents
MORE INFORMATION
KEYWORDS
Security, Data Loss Prevention, Distributed Networks, Networking, Virtual Testbed.
ABSTRACT
With data encryption, access control, and monitoring technology, high profile data breaches still occur. To address this issue, this work focused on securing data at rest and data in motion by utilizing current distributed network technology in conjunction with a data fragmenting and defragmenting algorithm. Software prototyping was used to exhaustively test this new paradigm within the confines of the Defense Technology Experimental Research (DETER) virtual testbed. The virtual testbed was used to control all aspects within the testing network including: node population, topology, file size, and number of fragments. In each topology, and for each population size, different sized files were fragmented, distributed to nodes on the network, recovered, and defragmented. All of these tests were recorded and documented. The results produced by this prototype showed, with the max wait time removed, an average wait time of .0287 s/fragment and by increasing the number of fragments, N, the complexity, X, would increase as demonstrated in the formula: X = (.00287N!).
CITED BY (0)  
1 Google Scholar
2 refSeek
3 Scribd
4 SlideShare
5 PdfSR
1 Kroll, K. Crafting an effective data security policy. Compliance Week, 10(110), 2013, 52-53.
2 Mace, S. Options in Data-Loss Prevention. Healthleaders Magazine, 15(11), 2012, 44-48.
3 Nah, F. F. (2004). A study on tolerable waiting time: how long are Web users willing to wait? Behaviour & Information Technology, 23(3), 2004, 153-163.
4 Meghanathan, N., Allam, S. R., & Moore, L. A. Tools and techniques for network forensics. In S. Fischer-Hubner & N. Hopper (Eds.), Privacy Enhancing Technologies (18-37). 2009, Canada: Springer-Verlag Berlin-Heidelberg.
5 Rapoza, J. (2008, May 5). Botnets vs. botnets. eWeek. p. 53.
6 Dixon, C., Anderson, T. E., & Krishnamurthy, A. (2008). Phalanx: Withstanding multimillion-node botnets. In NSDI, 8, 45-5.
7 Kroll, K. (2013). Crafting an effective data security policy. Compliance Week, 10(110), 52-53.
8 Hart, M., Manadhata, P., & Johnson, R. (2011, January). Text classification for data loss prevention. In Privacy Enhancing Technologies (pp. 18-37). Springer Berlin Heidelberg.
9 Venkatasubramanian, K. K. (2009). Security solutions for cyber-physical systems (Doctoral dissertation, Arizona State University).
10 Ezekiel, A. W. (2013). Hackers, spies, and stolen secrets: Protecting law firms from data theft. Harv. J. Law & Tec, 26, 649-695.
11 Liu, S., & Kuhn, R. (2010). Data loss prevention. IT professional, 12(2), 10-13.
Mr. Jacob Matthew Hadden
- United States of America
jacob.hadden@gmail.com
Dr. Ahmed M. Mahdy
Texas A&M University-Corpus Christi - United States of America