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POSTER: WinOver Enterprise Dark Data

Published: 12 October 2015 Publication History

Abstract

Any persistent untagged, untapped and unclassified data can be termed as dark data. It has two common traits: first, it is not possible to determine its worth, and second, in most of the scenarios it is inadequately protected. Previous work and existing solutions are restricted to cater single node system. Moreover, they perform specialized processing of selected content, for example, logs. Further, there is total negligence of stakeholders and minimal focus on the data getting generated within the enterprise. From the perspective of an enterprise it is important to understand the distribution, nature and worth of dark data, as it helps in choosing right security controls, insurance or steps needed to pre-process a system before discarding it. In this paper we demonstrate a distributed system, called File WinOver, for File Lifecycle Management (FLM). The solution operates in a distributed environment where it identifies the dormant and active files on a system, filters them as per requirement and computes their fingerprint. Moreover, the content fingerprinting is utilized to detect closed user groups. After which, it classifies the content based on configured policies, and maps them with the stakeholders. This mapping is further used for valuating the risk exposure of the file. Thus, our system helps in identifying dark data and assigns quantitative risk value.

References

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Young et al. Detecting unknown insider threat scenarios. In Security and Privacy Workshops (SPW), 2014 IEEE, pages 277--288. IEEE, 2014.
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Gates et al. Detecting insider information theft using features from file access logs. In Computer Security-ESORICS 2014, pages 383--400. Springer, 2014.
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Chen et al. Detection of anomalous insiders in collaborative environments via relational analysis of access logs. In Proceedings of the first ACM conference on Data and application security and privacy, pages 63--74. ACM, 2011.
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Beaver et al. An approach to the automated determination of host information value. In Computational Intelligence in Cyber Security (CICS), 2011 IEEE Symposium on, pages 92--99. IEEE, 2011.
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Park et al. Estimating asset sensitivity by profiling users. In Computer Security--ESORICS 2013, pages 94--110. Springer, 2013.
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Park et al. System for automatic estimation of data sensitivity with applications to access control and other applications. In Proceedings of the 16th ACM symposium on Access control models and technologies, pages 145--146. ACM, 2011.
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OWASP Risk Rating Methodology. https://www.owasp.org/index.php/OWASP_Risk_Rating_Methodology/.

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Published In

cover image ACM Conferences
CCS '15: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security
October 2015
1750 pages
ISBN:9781450338325
DOI:10.1145/2810103
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 October 2015

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Author Tags

  1. dark data
  2. data security
  3. data valuation

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CCS'15
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CCS '15 Paper Acceptance Rate 128 of 660 submissions, 19%;
Overall Acceptance Rate 1,261 of 6,999 submissions, 18%

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  • (2022)Enterprise SecurityResearch Anthology on Business Aspects of Cybersecurity10.4018/978-1-6684-3698-1.ch021(441-470)Online publication date: 2022
  • (2021)Deep Hash-based Relevance-aware Data Quality Assessment for Image Dark DataACM/IMS Transactions on Data Science10.1145/34200382:2(1-26)Online publication date: 8-Apr-2021
  • (2020)Enterprise SecurityAdvanced Digital Architectures for Model-Driven Adaptive Enterprises10.4018/978-1-7998-0108-5.ch008(158-187)Online publication date: 2020
  • (2020)A framework for image dark data assessmentWorld Wide Web10.1007/s11280-020-00779-xOnline publication date: 29-Feb-2020
  • (2019)A Framework for Image Dark Data AssessmentWeb and Big Data10.1007/978-3-030-26072-9_1(3-18)Online publication date: 18-Jul-2019
  • (2016)POSTERProceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security10.1145/2976749.2989051(1784-1786)Online publication date: 24-Oct-2016

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