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Spam Classification Based on E-Mail Path Analysis

Spam Classification Based on E-Mail Path Analysis

Palla Srikanth, Dantu W. Ram, Cangussu João
Copyright: © 2011 |Pages: 24
ISBN13: 9781616920005|ISBN10: 1616920009|ISBN13 Softcover: 9781616923709|EISBN13: 9781616920012
DOI: 10.4018/978-1-61692-000-5.ch021
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MLA

Srikanth, Palla, et al. "Spam Classification Based on E-Mail Path Analysis." Pervasive Information Security and Privacy Developments: Trends and Advancements, edited by Hamid Nemati, IGI Global, 2011, pp. 332-355. https://doi.org/10.4018/978-1-61692-000-5.ch021

APA

Srikanth, P., Ram, D. W., & João, C. (2011). Spam Classification Based on E-Mail Path Analysis. In H. Nemati (Ed.), Pervasive Information Security and Privacy Developments: Trends and Advancements (pp. 332-355). IGI Global. https://doi.org/10.4018/978-1-61692-000-5.ch021

Chicago

Srikanth, Palla, Dantu W. Ram, and Cangussu João. "Spam Classification Based on E-Mail Path Analysis." In Pervasive Information Security and Privacy Developments: Trends and Advancements, edited by Hamid Nemati, 332-355. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-61692-000-5.ch021

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Abstract

Email spam is the most effective form of online advertising. Unlike telephone marketing, email spamming does not require huge human or financial resources investment. Most existing spam filtering techniques concentrate on the emails’ content. However, most spammers obfuscate their emails’ content to circumvent content-based spam filters. An integrated solution for restricting spam emails is needed as content analyses alone might not provide a solution for filtering unsolicited emails. Here we present a new method for isolating unsolicited emails. Though spammers obfuscate their emails’ content, they do not have access to all the fields in the email header. Our classification method is based on the path an email traverses instead of content. Overall, our classifier produced fewer false positives when compared to current filters such as SpamAssassin. We achieved a precision of 98.65% which compares well with the precisions achieved by SPF, DNSRBL blacklists.

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