Plagiarism and copyright infringement are major problems in academic and corporate environments. Existing solutions for detecting infringements in structured text such as source code are restricted to textual similarity comparisons of two pieces of work. In this paper, we examine authorship attribution as a means for tackling plagiarism detection. Given several samples of work from several authors, we attempt to correctly identify the author of work presented as a query. On a collection of 1 640 documents written by 100 authors, we show that we can attribute authorship in up to 67 % of cases. This work can be a valuable additional indicator for the more difficult plagiarism investigations.