Revealing common sources of image spam by unsupervised clustering with visual features

ABSTRACT

In this paper, we investigate image spam with data mining techniques in order to reveal the common sources of unsolicited emails. To identify the origins, a two-stage clustering method groups visually similar spam images by exploring their visual features, including color feature, layout feature, text layout, and background textures. We test the proposed approach under different settings and combinations of features and measure the performance with a modified F-measure.

REFERENCES

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.3 Clustering
          Subjects: Algorithms

Additional Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.3 Clustering
          Subjects: Similarity measures

General Terms:
Algorithms, Experimentation, Security

Keywords:
botnet, clustering, computer forensics, image spam