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Detecting Spammers via Aggregated Historical Data Set

  • Eitan Menahem
  • Rami Pusiz
  • Yuval Elovici
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7645)

Abstract

In this work we propose a new sender reputation mechanism that is based on an aggregated historical dataset, which encodes the behavior of mail transfer agents over exponential growing time windows. The proposed mechanism is targeted mainly at large enterprises and email service providers and can be used for updating both the black and the white lists. We evaluate the proposed mechanism using 9.5M anonymized log entries obtained from the biggest Internet service provider in Europe. Experiments show that proposed method detects more than 94% of the Spam emails that escaped the blacklist (i.e., TPR), while having less than 0.5% false-alarms. Therefore, the effectiveness of the proposed method is much higher than of previously reported reputation mechanisms, which rely on emails logs. In addition, on our data-set the proposed method eliminated the need in automatic content inspection of 4 out of 5 incoming emails, which resulted in dramatic reduction in the filtering computational load.

Keywords

Aggregate Feature Spam Email Black List Address Error History Length 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Eitan Menahem
    • 1
  • Rami Pusiz
    • 1
  • Yuval Elovici
    • 1
  1. 1.Telekom Innovation Laboratories, Information System Engineering DepartmentBen-Gurion UniversityBe’er ShevaIsrael

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