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Genetic optimized artificial immune system in spam detection: a review and a model

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Abstract

Spam is a serious universal problem which causes problems for almost all computer users. This issue affects not only normal users of the internet, but also causes a big problem for companies and organizations since it costs a huge amount of money in lost productivity, wasting users’ time and network bandwidth. Many studies on spam indicate that spam cost organizations billions of dollars yearly. This work presents a machine learning method inspired by the human immune system called Artificial Immune System (AIS) which is a new emerging method that still needs further exploration. Core modifications were applied on the standard AIS with the aid of the Genetic Algorithm. Also an Artificial Neural Network for spam detection is applied with a new manner. SpamAssassin corpus is used in all our simulations.

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Authors and Affiliations

  1. Department of Computer Science, School of Engineering and Computing Sciences, New York Institute of Technology, Amman, Jordan

    Raed Abu Zitar

  2. Department of Computer Science, Applied Science University, Amman, Jordan

    Adel Hamdan

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  1. Raed Abu Zitar
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  2. Adel Hamdan
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Correspondence to Raed Abu Zitar.

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Zitar, R.A., Hamdan, A. Genetic optimized artificial immune system in spam detection: a review and a model. Artif Intell Rev 40, 305–377 (2013). https://doi.org/10.1007/s10462-011-9285-z

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