DocumentCode :
3712890
Title :
Automatic security classification by machine learning for cross-domain information exchange
Author :
Hugo Hammer;Kyrre Wahl Kongsg?rd;Aleksander Bai;Anis Yazidi;Nils Agne Nordbotten;Paal E. Engelstad
Author_Institution :
Oslo and Akershus University College of Applied Sciences (HiOA), Norway
fYear :
2015
Firstpage :
1590
Lastpage :
1595
Abstract :
Cross-domain information exchange is necessary to obtain information superiority in the military domain, and should be based on assigning appropriate security labels to the information objects. Most of the data found in a defense network is unlabeled, and usually new unlabeled information is produced every day. Humans find that doing the security labeling of such information is labor-intensive and time consuming. At the same time there is an information explosion observed where more and more unlabeled information is generated year by year. This calls for tools that can do advanced content inspection, and automatically determine the security label of an information object correspondingly. This paper presents a machine learning approach to this problem. To the best of our knowledge, machine learning has hardly been analyzed for this problem, and the analysis on topical classification presented here provides new knowledge and a basis for further work within this area. Presented results are promising and demonstrates that machine learning can become a useful tool to assist humans in determining the appropriate security label of an information object.
Keywords :
"Labeling","Information exchange","Computer security","Electronic mail","Digital signatures","Metadata"
Publisher :
ieee
Conference_Titel :
Military Communications Conference, MILCOM 2015 - 2015 IEEE
Type :
conf
DOI :
10.1109/MILCOM.2015.7357672
Filename :
7357672
Link To Document :
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