Traditional spam filters are not adequately detecting these undesirable emails, and this causes problems for both consumers and businesses wishing to do business online. From a learning perspective, this is a challenging problem. At first glance, the problem appears to be a simple text classification problem, but the classification is confounded by the fact that the class of ¡°phishing¡± emails is nearly identical to the class of real emails. We propose a new method for detecting these malicious emails called PILFER. By incorporating features specifically designed to highlight the deceptive methods used to fool users, we are able to accurately classify over 92% of phishing emails, while maintaining a false positive rate on the order of 0.1%. These results are obtained on a dataset of approximately 860 phishing emails and 6950 non-phishing emails. The accuracy of PILFER on this dataset is significantly better than that of SpamAssassin, a widely-used spam filter.
