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
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).
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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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DOI: https://doi.org/10.1007/s00500-015-1959-z