Skip to main content

Data Leakage Detection/Prevention Solutions

  • Chapter
  • First Online:
Book cover A Survey of Data Leakage Detection and Prevention Solutions

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

Abstract

According to the Forrester Wave report [Raschke, 2008], most early DLP solutions focused on finding sensitive data as they left the organizational network by monitoring data-in-motion at the various network egress points. In the second stage, as removable storage devices (e.g., USB sticks, external hard drives) proliferated, DLP solutions began to focus on detecting data leakage at the endpoint and on providing capabilities, for example, to subvert copying of sensitive information to USB devices or CD/DVDs even if the endpoint is not connected to the network.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://www.cs.cmu.edu/enron

References

  • Byers, S. 2004. Information leakage caused by hidden data in published documents. IEEE Security and Privacy, 2(2), 23–27.

    Article  MathSciNet  Google Scholar 

  • Caputo D.D., Stephens G.D., and Maloof M.A. 2009. Detecting insider theft of trade secrets. IEEE Security and Privacy, 7(6), 14–21.

    Article  Google Scholar 

  • Carvalho, V.R., and Cohen, W. 2007. Preventing information leaks in email. Proceedings, SIAM International Conference on Data Mining.

    Google Scholar 

  • Cohen, W.W. 1996. Learning rules that classify e-mail. Proceedings, AAAI Symposium on Machine Learning in Information Access, 18–25.

    Google Scholar 

  • Forte, D. 2009. Do encrypted disks spell the end of forensics? Computer Fraud and Security, 2009(2), 18–20.

    Article  Google Scholar 

  • Frost & Sullivan. 2008. World Data Leakage Prevention Market. Technical Report ND34D-74, Frost & Sullivan, United States.

    Google Scholar 

  • Hong, J., Kim, J., and Cho, J. 2010. The trend of the security research for the insider cyber threat. International Journal of Future Generation Communication and Networking, 3(2), 31–40.

    Google Scholar 

  • Hovold, J. 2005. Naive Bayes span filtering using word-position-based attributes. Proceedings, 2nd Conference on Email and Anti-Spam.

    Google Scholar 

  • Kamra, A., Terzi, E., Evimaria, and Bertino, E. 2008. Detecting anomalous access patterns in relational databases. International Journal on Very Large Databases, 17(5), 1063–1077.

    Article  Google Scholar 

  • Lawton, G. 2008. New technology prevents data leakage. Computer, 41(9), 14–17.

    Article  Google Scholar 

  • Mun, H., Han, K., Yeun, C.Y., and Kim, K. 2008. Yet another intrusion detection system against Insider Attacks. Proceesings, Symposium on Cryptography and Information Security.

    Google Scholar 

  • Parno, B., McCune, J.M., Wendlandt, D., Andersen, D.G., and Perrig, A. 2009. CLAMP: practical prevention of large-scale data leaks. Proceedings, IEEE Symposium on Security and Privacy.

    Google Scholar 

  • Raschke, T. 2008. The Forrester Wave™: Data Leak Prevention, Q2 2008. Technical report, Forrester Research, Inc.

    Google Scholar 

  • Rennie, J. 2000. ifile: an application of machine learning to e-mail filtering. Proceedings, KDD-Workshop on Text Mining.

    Google Scholar 

  • Salem, B.M., Heshkop, S., and Stolfo, S.J. 2008. A survey of insider attack detection eesearch. Insider Attack and Cyber Security- Beyond the Hacker, Springer, 39, 23–27.

    Google Scholar 

  • Spitzner, L. 2003. Honeypots: catching the insider threat. Proceedings, 19th Annual Computer Security Applications Conference (ACSAC’03), 170–179.

    Google Scholar 

  • Storey, D. 2009. Catching flies with honey tokens. Network Security, 2009(11), 15–18.

    Article  Google Scholar 

  • Valli, C. 2005. Honeypot technologies and their applicability as a strategic internal countermeasure. International Journal of Information and Computer Security, 1(4), 30–436.

    MathSciNet  Google Scholar 

  • White, J. 2010. Creating personally identifiable honeytokens. Innovations and Advances in Computer Sciences and Engineering, 227–232.

    Google Scholar 

  • White, J., and Panda, B. 2010. Insider threat discovery using automatic detection of mission critical data based on content. Proceedings, Sixth International Conference on Information Assurance and Security (IAS), IEEE, pp. 56–61.

    Google Scholar 

  • Yixiang, S., Tao, P., and Minghua, J. 2007. Secure multiple XML documents publishing without information leakage. Proceedings, International Conference on Convergence Information Technology, 2114–2119.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2012 The Author(s)

About this chapter

Cite this chapter

Shabtai, A., Elovici, Y., Rokach, L. (2012). Data Leakage Detection/Prevention Solutions. In: A Survey of Data Leakage Detection and Prevention Solutions. SpringerBriefs in Computer Science. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-2053-8_4

Download citation

  • DOI: https://doi.org/10.1007/978-1-4614-2053-8_4

  • Published:

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4614-2052-1

  • Online ISBN: 978-1-4614-2053-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics