Abstract
The ubiquitous presences of internet and network technologies have enabled electronic mail systems as the primary medium of communication. Both between and within organizations, sensitive and personal information often transits through the electronic mail systems undetected. Information leakage through this mode of communication has become a daunting problem in today’s world. Often the mail volume within an organization is quite large making manual monitoring impossible. In this paper an integration of secure information flow techniques on intranet electronic mail systems is investigated. Categorization of emails based on the sensitivity is accomplished effectively using machine learning techniques. Analyzing the information flow and simultaneously mapping, categorizing and sorting emails in real time prior to receipt of emails has been characterized in this study. Defining security policies and application of lattice models for controlled exchange of emails is discussed. The paper proposed a secure architecture for an email web application. Experimental analysis on the accuracy of the application was determined using Enron email dataset.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Email Statistics Report (2013-2017), www.radicati.com/wp/wp-content/uploads/2013/04/Email-Statistics-Report-2013-2017-Executive-Summary.pdf
Silver Sky Email Security Habits Survey Report (2013), https://www.silversky.com/knowledge-center/white-papers/silversky-email-security-habits-survey-report
Shabtai, A., Elovici, Y., Rokach, L.: A survey of data leakage detection and prevention solutions, pp. 1–46. Springer (2012)
Majority of Business Professionals Have Sent E-mails to the Wrong Person (2013), www.esecurityplanet.com/network-security/majority-of-business-professionals-have-sent-e-mails-to-the-wrong-person.html
Achieving End-to-End Email, http://www.voltage.com/wp-content/uploads/Voltage_DS_SecureMail.pdf
Which Disney© Princess are YOU? (2010), www.sans.org/reading-room/whitepapers/engineering/disney-princess-you-33328
Liu, S., Kuhn, R.: Data Loss Prevention. IEEE IT Professional 12, 10–13 (2010)
Agarwal, A., Gaikwad, M., Garg, K., Inamdar, V.: Robust Data leakage and Email Filtering System. In: International Conference on Computing, Electronics and Electrical Technologies (ICCEET), pp. 1032–1035 (2012)
Grobelnik, M., Mladenic, D., Fortuna, B.: Semantic technology for capturing communication inside an organization. IEEE Internet Computing, 59–67 (2013)
Mcafee Total Protection for Data Loss Prevention (2013), http://www.mcafee.com/in/resources/solution-briefs/sb-total-protection-for-dlp.pdf
Zilberman, P., Dolev, S., Katz, G., Elovici, Y., Shabtai, A.: Analyzing Group Communication for Preventing Data Leakage via Email. In: IEEE International Conference on Intelligence and Security Informatics (ISI), pp. 37–41 (2011)
Simanjuntak, D.A., Ipung, H.P., Lim, C., Nugroho, A.S.: Text Classification Techniques Used to Facilitate Cyber Terrorism Investigation. In: IEEE 2nd International Conference on Advances in Computing, Control, and Telecommunication Technologies, pp. 198–200 (2010)
Vira, D., Raja, P., Gada, S.: An Approach to Email Classification Using Bayesian Theorem. Global Journal of Computer Science and Technology Software Data Engineering 12(13) (2012)
Ting, S.L., Ip, W.H., Tsang, A.H.C.: Is Naïve Bayes a Good Classifier for Document Classification? International Journal of Software Engineering and Its Applications 5(3), 37–46 (2011)
Azam, N., Yao, J.: Comparison of term frequency and document frequency based feature selection metrics in text categorization. Elsevier 39(5), 4760–4768 (2012)
Zhang, W., Yoshida, T., Tang, X.: TFIDF, LSI and Multi-word in Information Retrieval and Text Categorization. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 108–113 (2008)
Rahman, A., Babri, H.A., Saeed, M.: Feature Extraction Algorithms for Classification of Text Documents. In: International Conference on Computer and Information Technology, pp. 231–236 (2012)
Uysal, A.K., Gunal, S.: A novel probabilistic feature selection method for text classification. Knowledge-Based Systems 36, 226–235 (2012)
Wei, Z., Zhang, H., Zhang, Z., Li, W., Miao, D.: A Naive Bayesian Multi-label Classification Algorithm With Application to Visualize Text Search Results. International Journal of Advanced Intelligence 3(2), 173–188 (2011)
Dredze, M., Wallach, H.M., Puller, D., Pereira, F.: Generating Summary Keywords for Emails Using Topics. In: Proc. 13th ACM International Conference on Intelligent User Interfaces, pp. 199–206. ACM (2008)
Denning, D.E.: Lattice Model of Secure Information Flow. Communications of the ACM 19(5), 236–243 (1976)
Sandhu, R.S.: Lattice-Based Access Control Models. IEEEComputer 26(11), 9–19 (1993)
Ying, S., Lirong, X.: Lattice based BLP Extended Model. In: Second International Conference on Future Information Technology and Management Engineering, pp. 309–312 (2009)
Diesner, J., Frantz, T.L., Carley, K.M.: Communication networks from the Enron email corpus “It’s always about the people. Enron is no different”. Computational & Mathematical Organization Theory 11(3), 201–228 (2005)
Wang, M., He, Y., Jiang, M.: Text categorization of Enron email corpus based on information bottleneck and maximal entropy. In: IEEE 10th International Conference on Signal Processing (ICSP), pp. 2472–2475 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Manmadhan, N., Narayanan, H., Poroor, J., Achuthan, K. (2014). Design for Prevention of Intranet Information Leakage via Emails. In: Mauri, J.L., Thampi, S.M., Rawat, D.B., Jin, D. (eds) Security in Computing and Communications. SSCC 2014. Communications in Computer and Information Science, vol 467. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44966-0_13
Download citation
DOI: https://doi.org/10.1007/978-3-662-44966-0_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-44965-3
Online ISBN: 978-3-662-44966-0
eBook Packages: Computer ScienceComputer Science (R0)