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Email Worm Detection Using Data Mining

Email Worm Detection Using Data Mining

Mohammad M. Masud, Latifur Khan, Bhavani Thuraisingham
Copyright: © 2009 |Pages: 15
ISBN13: 9781605662107|ISBN10: 1605662100|ISBN13 Softcover: 9781616924997|EISBN13: 9781605662114
DOI: 10.4018/978-1-60566-210-7.ch002
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MLA

Masud, Mohammad M., et al. "Email Worm Detection Using Data Mining." Techniques and Applications for Advanced Information Privacy and Security: Emerging Organizational, Ethical, and Human Issues, edited by Hamid Nemati, IGI Global, 2009, pp. 20-34. https://doi.org/10.4018/978-1-60566-210-7.ch002

APA

Masud, M. M., Khan, L., & Thuraisingham, B. (2009). Email Worm Detection Using Data Mining. In H. Nemati (Ed.), Techniques and Applications for Advanced Information Privacy and Security: Emerging Organizational, Ethical, and Human Issues (pp. 20-34). IGI Global. https://doi.org/10.4018/978-1-60566-210-7.ch002

Chicago

Masud, Mohammad M., Latifur Khan, and Bhavani Thuraisingham. "Email Worm Detection Using Data Mining." In Techniques and Applications for Advanced Information Privacy and Security: Emerging Organizational, Ethical, and Human Issues, edited by Hamid Nemati, 20-34. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-210-7.ch002

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Abstract

This chapter applies data mining techniques to detect email worms. Email messages contain a number of different features such as the total number of words in message body/subject, presence/absence of binary attachments, type of attachments, and so on. The goal is to obtain an efficient classification model based on these features. The solution consists of several steps. First, the number of features is reduced using two different approaches: feature-selection and dimension-reduction. This step is necessary to reduce noise and redundancy from the data. The feature-selection technique is called Two-phase Selection (TPS), which is a novel combination of decision tree and greedy selection algorithm. The dimensionreduction is performed by Principal Component Analysis. Second, the reduced data is used to train a classifier. Different classification techniques have been used, such as Support Vector Machine (SVM), Naïve Bayes and their combination. Finally, the trained classifiers are tested on a dataset containing both known and unknown types of worms. These results have been compared with published results. It is found that the proposed TPS selection along with SVM classification achieves the best accuracy in detecting both known and unknown types of worms.

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