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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 subdictionary. 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. 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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How to cite:Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.

ID theft, especially in its online form, is currently one of the most prevalent types of computer crime. The limited end-user awareness as well as the retention and business processing of large amounts of personal data in a manner that does not meet security and regulatory requirements provide plenty of opportunities to fraudsters. A number of organisations have produced guidelines of good practice targeted to individuals and organisations, however the matter is still on the rise. In this paper, we review computer-based techniques employed by fraudsters in order to steal IDs and refer to published guidelines and the documented good practice against those. We discuss the issues related to the investigation of such incidents and provide the grounds for the development of a framework to assist in their forensic examination.