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A hybrid spam detection method based on unstructured datasets

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Abstract

The identification of non-genuine or malicious messages poses a variety of challenges due to the continuous changes in the techniques utilised by cyber-criminals. In this article, we propose a hybrid detection method based on a combination of image and text spam recognition techniques. In particular, the former is based on sparse representation-based classification, which focuses on the global and local image features, and a dictionary learning technique to achieve a spam and a ham sub-dictionary. On the other hand, the textual analysis is based on semantic properties of documents to assess the level of maliciousness. More specifically, we are able to distinguish between meta-spam and real spam. Experimental results show the accuracy and potential of our approach.

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Notes

  1. This database is available at http://www.cs.jhu.edu/~mdredze/datasets/image_spam/.

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Acknowledgments

The paper is supported by the National Science Foundation of China (61171132), the Natural Science Foundation of Jiangsu Province (BK2015022392), the Talent Project of Jiangsu Province of China (2014WLW029), and the Technology Platform Projects of Nantong (CP2013001).

Author information

Authors and Affiliations

  1. Department of Computing and Mathematics, University of Derby, Derby, UK

    Yeqin Shao, Marcello Trovati, Quan Shi & Eleana Asimakopoulou

  2. Computer Science Department, University of Hertfordshire, Hatfield, Hertfordshire, AL10 9AB, UK

    Olga Angelopoulou

  3. Department of Computing, Edge Hill University, Ormskirk, Lancashire, L39 4QP, UK

    Nik Bessis

  4. Institute of Image Processing & Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240, China

    Yeqin Shao

  5. School of Computer Science and Technology, Nantong University, Nantong, 226019, Jiangsun, China

    Quan Shi

Authors
  1. Yeqin Shao
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  2. Marcello Trovati
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  3. Quan Shi
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  4. Olga Angelopoulou
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  5. Eleana Asimakopoulou
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  6. Nik Bessis
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Corresponding author

Correspondence to Marcello Trovati.

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The authors declare that they have no conflict of interest.

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Communicated by V. Loia.

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Shao, Y., Trovati, M., Shi, Q. et al. A hybrid spam detection method based on unstructured datasets. Soft Comput 21, 233–243 (2017). https://doi.org/10.1007/s00500-015-1959-z

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