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Detecting Malicious Spam Mails: An Online Machine Learning Approach

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 8836)


Malicious spam is one of the major problems of the Internet nowadays. It brings financial damage to companies and security threat to governments and organizations. Most recent spam emails contain URLs that redirect spam receivers to malicious Web servers. In this paper, we propose an online machine learning based malicious spam email detection system. The term-weighting scheme represents each spam email. These feature vectors are then used as the input of the classifier. The learning is periodically performed to update the classifier so that the system provides increased adaptability to take account of spam emails whose contents change from time to time. A real data set is labeled by the SPIKE system which is developed by NICT. Evaluation experiments show that the detection system is efficient and accurate to identify malicious spam emails.


  • malicious spam detection
  • online learning
  • tf-idf
  • vector space model

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  • DOI: 10.1007/978-3-319-12643-2_45
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© 2014 Springer International Publishing Switzerland

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Dai, Y., Tada, S., Ban, T., Nakazato, J., Shimamura, J., Ozawa, S. (2014). Detecting Malicious Spam Mails: An Online Machine Learning Approach. In: Loo, C.K., Yap, K.S., Wong, K.W., Beng Jin, A.T., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8836. Springer, Cham.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12642-5

  • Online ISBN: 978-3-319-12643-2

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