Mining writeprints from anonymous e-mails for forensic investigation

Many criminals exploit the convenience of anonymity in the cyber world to conduct illegal activities. E-mail is the most commonly used medium for such activities. Extracting knowledge and information from e-mail text has become an important step for cybercrime investigation and evidence collection. Yet, it is one of the most challenging and time-consuming tasks due to special characteristics of e-mail dataset. In this paper, we focus on the problem of mining the writing styles from a collection of e-mails written by multiple anonymous authors. The general idea is to first cluster the anonymous e-mail by the stylometric features and then extract the writeprint, i.e., the unique writing style, from each cluster. We emphasize that the presented problem together with our proposed solution is different from the traditional problem of authorship identification, which assumes training data is available for building a classifier. Our proposed method is particularly useful in the initial stage of investigation, in which the investigator usually have very little information of the case and the true authors of suspicious e-mail collection. Experiments on a real-life dataset suggest that clustering by writing style is a promising approach for grouping e-mails written by the same author.