Applying Unsupervised Context-Based Analysis for Detecting Unauthorized Data Disclosure

Ma'ayan Gafny; Asaf Shabtai; Lior Rokach; Yuval Elovici

Applying Unsupervised Context-Based Analysis for Detecting Unauthorized Data Disclosure

Ma'ayan Gafny, Asaf Shabtai, Lior Rokach, Yuval Elovici

Research output: Contribution to conference › Poster › peer-review

Abstract

In this paper, we propose a new unsupervised approach for identifying suspicious access to sensitive relational data. In the proposed method, a tree-like model encapsulates the characteristics of the result-set (i.e., data) that the user normally access within each possible context. During the detection phase, result-sets are examined against the induced model and a similarity score is derived.

Original language	American English
Pages	765-768
Number of pages	4
State	Published - 2011
Event	18th ACM Conference on Computer and Communications Security - Chicago, United States Duration: 17 Oct 2011 → 21 Oct 2011 https://dblp.org/db/conf/ccs/ccs2011.html#GafnySRE11

Conference

Conference	18th ACM Conference on Computer and Communications Security
Abbreviated title	18th CCS 2011
Country/Territory	United States
City	Chicago
Period	17/10/11 → 21/10/11
Internet address	https://dblp.org/db/conf/ccs/ccs2011.html#GafnySRE11

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abstract = "In this paper, we propose a new unsupervised approach for identifying suspicious access to sensitive relational data. In the proposed method, a tree-like model encapsulates the characteristics of the result-set (i.e., data) that the user normally access within each possible context. During the detection phase, result-sets are examined against the induced model and a similarity score is derived.",

author = "Ma'ayan Gafny and Asaf Shabtai and Lior Rokach and Yuval Elovici",

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TY - CONF

T1 - Applying Unsupervised Context-Based Analysis for Detecting Unauthorized Data Disclosure

AU - Gafny, Ma'ayan

AU - Shabtai, Asaf

AU - Rokach, Lior

AU - Elovici, Yuval

N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

PY - 2011

Y1 - 2011

N2 - In this paper, we propose a new unsupervised approach for identifying suspicious access to sensitive relational data. In the proposed method, a tree-like model encapsulates the characteristics of the result-set (i.e., data) that the user normally access within each possible context. During the detection phase, result-sets are examined against the induced model and a similarity score is derived.

AB - In this paper, we propose a new unsupervised approach for identifying suspicious access to sensitive relational data. In the proposed method, a tree-like model encapsulates the characteristics of the result-set (i.e., data) that the user normally access within each possible context. During the detection phase, result-sets are examined against the induced model and a similarity score is derived.

M3 - Poster

SP - 765

EP - 768

T2 - 18th ACM Conference on Computer and Communications Security

Y2 - 17 October 2011 through 21 October 2011

ER -

Applying Unsupervised Context-Based Analysis for Detecting Unauthorized Data Disclosure

Abstract

Conference

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