Learning DOM Trees of Web Pages by Subpath Kernel and Detecting Fake e-Commerce Sites

Abstract

The subpath kernel is a class of positive definite kernels defined over trees, which has the following advantages for the purposes of classification, regression and clustering: it can be incorporated into a variety of powerful kernel machines including SVM; It is invariant whether input trees are ordered or unordered; It can be computed by significantly fast linear-time algorithms; And, finally, its excellent learning performance has been proven through intensive experiments in the literature. In this paper, we leverage recent advances in tree kernels to solve real problems. As an example, we apply our method to the problem of detecting fake e-commerce sites. Although the problem is similar to phishing site detection, the fact that mimicking existing authentic sites is harmful for fake e-commerce sites marks a clear difference between these two problems. We focus on fake e-commerce site detection for three reasons: e-commerce fraud is a real problem that companies and law enforcement have been cooperating to solve; Inefficiency hampers existing approaches because datasets tend to be large, while subpath kernel learning overcomes these performance challenges; And we offer increased resiliency against attempts to subvert existing detection methods through incorporating robust features that adversaries cannot change: the DOM-trees of web-sites. Our real-world results are remarkable: our method has exhibited accuracy as high as 0.998 when training SVM with 1000 instances and evaluating accuracy for almost 7000 independent instances. Its generalization efficiency is also excellent: with only 100 training instances, the accuracy score reached 0.996.