NDLI: A Small Data Approach to Identification of Individuals on the Transport Layer using Statistical Behaviour Templates

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A Small Data Approach to Identification of Individuals on the Transport Layer using Statistical Behaviour Templates

Content Provider	ACM Digital Library
Author	Abt, Sebastian Gärtner, Sebastian Baier, Harald
Abstract	Our daily life is dominated by constant Internet connectivity. In order to retrieve up-to-date information and to share personal experiences and impressions, our computers and mobile devices periodically communicate with servers or peers. During this data exchange, we constantly leave digital traces on different systems across the communication stack. These traces can be used to compute profiles of individuals. While such profiles may be used to increase user experience and convenience, they seriously affect privacy of individuals. Typically, service providers like Google or Facebook collect gigabytes to terabytes of user payload data to compute user profiles from. That is, they make use of a big data approach. In contrast to that, this paper shows a novel small data approach to compute profiles using behaviour templates derived from IP address and port number statistics. Our use case is to increase network security through concurrent identification. Our approach is capable of identifying individuals with true- and false-positive rates of 0.995 and 0.001, respectively, without relying on payload information, significantly outperforming related work.
Starting Page	25
Ending Page	32
Page Count	8
File Format	PDF
ISBN	9781450330336
DOI	10.1145/2659651.2659681
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-09-09
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Networks Privacy Profiling Behaviour Communication
Content Type	Text
Resource Type	Article

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