Utilizing Innovative Techniques to Improve Email Security
This paper proposes a technique to protect against
email bombing. The technique employs a statistical approach, Naïve
Bayes (NB), and Neural Networks to show that it is possible to
differentiate between good and bad traffic to protect against email
bombing attacks. Neural networks and Naïve Bayes can be trained
by utilizing many email messages that include both input and output
data for legitimate and non-legitimate emails. The input to the model
includes the contents of the body of the messages, the subject, and
the headers. This information will be used to determine if the email
is normal or an attack email. Preliminary tests suggest that Naïve
Bayes can be trained to produce an accurate response to confirm
which email represents an attack.
[1] Carnegie Mellon University. The Computer Emergency Response Team.
(2002, March) Internet Denial of Service Attacks and the Federal
Response. http://www.cert.org
[2] Langley, P. 1992. Systematic and nonsystematic search strategies. In
Proceedings of the First International Conference on Artificial
Intelligence Planning Systems, pp. 145-152 College Park, Maryland.
Morgan Kaufmann.
[3] Lindberg, G. "Anti-Spam Recommend-ations for SMTP MTAs" Internet
RFC 2505, February 1999.
[4] M. Aery and S. Chakravarthy. eMailSift: Adapting Graph Mining
Techniques for Email Classification: Technical Report. University of
Texas at Arlington. 2004.
[5] J. Catlett. Megainduction: A test ┬░ight. International Conference on
Machine Learning, 1991.
[6] W. W. Cohen. Learning rules that classify e-mail. Proceedings of AAAI-
1996 Spring Symposium on Machine Learning in Information Access,
pp. 145- 155.
[7] J. Herman and C. Isbell. Ishmail: Immediate identification of important
information, AT&T labs. 1995.
[8] J. D. M.Rennie. Application of machine learning to e-mail filtering.
Proceedings of KDD-2000 Text Mining Workshop, Boston Aug, 2000.
[9] T. Payne and P. Edwards. Interface agents that learn: An investigation of
learning issues in a mail agent interface. Applied Artificial Intelligence,
pp 132-144. 1997.
[10] J. Rissanen. Stochastic complexity in statistical enquiry. World
Publishing Company, 1989.
[11] M. Sahami, D. Heckerman, and E. Horovitz. A Bayesian approach to
filtering junk e-mail. AAAI-98 Workshop on Learning for Text
Categorization, 1998.
[12] Androutsopoulos, G. Paliouras, V. Karkaletsis, G. Sakkis, C.D.
Spyropoulos and P. Stamatopoulos, "Learning to Filter Spam E-Mail: A
Comparison of a Naive Bayesian and a Memory-Based Approach". In H.
Zaragoza, P. Gallinari, and M. Rajman (Eds.), Proceedings of the
Workshop on Machine Learning and Textual Information Access, 4th
European Conference on Principles and Practice of Knowledge
Discovery in Databases (PKDD 2000), Lyon, France, pp. 1-13, 2000.
[1] Carnegie Mellon University. The Computer Emergency Response Team.
(2002, March) Internet Denial of Service Attacks and the Federal
Response. http://www.cert.org
[2] Langley, P. 1992. Systematic and nonsystematic search strategies. In
Proceedings of the First International Conference on Artificial
Intelligence Planning Systems, pp. 145-152 College Park, Maryland.
Morgan Kaufmann.
[3] Lindberg, G. "Anti-Spam Recommend-ations for SMTP MTAs" Internet
RFC 2505, February 1999.
[4] M. Aery and S. Chakravarthy. eMailSift: Adapting Graph Mining
Techniques for Email Classification: Technical Report. University of
Texas at Arlington. 2004.
[5] J. Catlett. Megainduction: A test ┬░ight. International Conference on
Machine Learning, 1991.
[6] W. W. Cohen. Learning rules that classify e-mail. Proceedings of AAAI-
1996 Spring Symposium on Machine Learning in Information Access,
pp. 145- 155.
[7] J. Herman and C. Isbell. Ishmail: Immediate identification of important
information, AT&T labs. 1995.
[8] J. D. M.Rennie. Application of machine learning to e-mail filtering.
Proceedings of KDD-2000 Text Mining Workshop, Boston Aug, 2000.
[9] T. Payne and P. Edwards. Interface agents that learn: An investigation of
learning issues in a mail agent interface. Applied Artificial Intelligence,
pp 132-144. 1997.
[10] J. Rissanen. Stochastic complexity in statistical enquiry. World
Publishing Company, 1989.
[11] M. Sahami, D. Heckerman, and E. Horovitz. A Bayesian approach to
filtering junk e-mail. AAAI-98 Workshop on Learning for Text
Categorization, 1998.
[12] Androutsopoulos, G. Paliouras, V. Karkaletsis, G. Sakkis, C.D.
Spyropoulos and P. Stamatopoulos, "Learning to Filter Spam E-Mail: A
Comparison of a Naive Bayesian and a Memory-Based Approach". In H.
Zaragoza, P. Gallinari, and M. Rajman (Eds.), Proceedings of the
Workshop on Machine Learning and Textual Information Access, 4th
European Conference on Principles and Practice of Knowledge
Discovery in Databases (PKDD 2000), Lyon, France, pp. 1-13, 2000.
@article{"International Journal of Information, Control and Computer Sciences:53665", author = "Amany M. Alshawi and Khaled Alduhaiman", title = "Utilizing Innovative Techniques to Improve Email Security", abstract = "This paper proposes a technique to protect against
email bombing. The technique employs a statistical approach, Naïve
Bayes (NB), and Neural Networks to show that it is possible to
differentiate between good and bad traffic to protect against email
bombing attacks. Neural networks and Naïve Bayes can be trained
by utilizing many email messages that include both input and output
data for legitimate and non-legitimate emails. The input to the model
includes the contents of the body of the messages, the subject, and
the headers. This information will be used to determine if the email
is normal or an attack email. Preliminary tests suggest that Naïve
Bayes can be trained to produce an accurate response to confirm
which email represents an attack.", keywords = "Email bombing, Legitimate email, Naïve Bayes, Neural networks, Non-legitimate email.", volume = "7", number = "6", pages = "734-5", }