Methods and systems for phishing detection
A method of determining a probability that a received email comprises a phishing attempt may comprise analyzing a link therein to determine whether the link comprises a phishing attempt. This determination may comprise comparing features of the link with records stored in a remote database to determine whether the link comprises a phishing attempt. It may be determined that the link comprises a phishing attempt if there is a match. If the compared features do not match the records stored in the remote database, a multi-dimensional input vector may be built from features of the link, which input vector may then be input into a phishing probability engine. The probability that the link comprises a phishing attempt may be computed by the phishing probability engine. Thereafter, the received email may be acted upon according to the computed probability that the link comprises a phishing attempt.
1. A method of determining a probability that a received email comprises a phishing attempt, comprising:
receiving an email,
analyzing a link within the email to determine whether the link comprises a phishing attempt by:
comparing at least some features of the link with records stored in a remote database to determine whether the link comprises a phishing attempt and determining that the link comprises a phishing attempt if the compared features match the records stored in the remote database;
building a multi-dimensional input vector from at least features of the link if the compared features do not match the records stored in the remote database and evaluating a plurality of the features of the link to set or reset at least one bit of the multi-dimensional input vector;
inputting the built multi-dimensional input vector into a phishing probability engine;
computing, in the phishing probability engine, a probability that the link comprises a phishing attempt; and
acting upon the received email depending upon the computed probability that the link comprises a phishing attempt.
2. The method of claim 1 , wherein the link comprises a Uniform Resource Locator (URL).
3. The method of claim 1 , wherein the features of the link comprise at least one of a domain name, a subdomain name, a path, a symbol, a keyword, a document title and a meta description.
4. The method of claim 1 , wherein at least some of the compared features are brand-specific and at least some of the compared features are not brand-specific.
5. The method of claim 1 , wherein building comprises building the multi-dimensional input vector from the features of the link and from content of the webpage pointed to by the link.
6. The method of claim 1 , wherein the phishing probability engine comprises a Support Vector Machine (SVM) classifier.
7. The method of claim 6 , further comprising training the SVM classifier with a corpus of non-phishing elements and phishing elements to generate a SVM model that is configured to be used by the phishing probability engine.
8. The method of claim 7 , wherein the corpus comprises elements that comprise a first file that comprises a hash of the link and a selected quantity and a second file that comprises a hash of contents of a webpage pointed to by the link and the selected quantity.
9. The method of claim 8 , wherein the hash comprises a message digest algorithm.
10. The method of claim 1 , further comprising attempting to identify a brand that is a focus of at least one of the received email and the link.
11. The method of claim 1 , wherein comparing is carried out with the remote database storing a plurality of records, each of which comprising a logical construct that defines a brand.
12. The method of claim 10 , wherein the logical construct is formatted as a Document Type Definition (DTD).
13. A computing device configured to determine a probability that a received email comprises a phishing attempt, comprising:
at least one processor;
at least one data storage device coupled to the at least one processor;
a plurality of processes spawned by said at least one processor, the processes including processing logic for:
receiving an email,
analyzing a link within the email to determine whether the link comprises a phishing attempt by:
comparing at least some features of the link with records stored in a remote database to determine whether the link comprises a phishing attempt and determining that the link comprises a phishing attempt if the compared features match the records stored in the remote database;
building a multi-dimensional input vector from at least features of the link if the compared features do not match the records stored in the remote database and evaluating a plurality of the features of the link to set or reset at least one bit of the multi-dimensional input vector;
inputting the built multi-dimensional input vector into a phishing probability engine;
computing, in the phishing probability engine, a probability that the link comprises a phishing attempt; and
acting upon the received email depending upon the computed probability that the link comprises a phishing attempt.
14. The computing device of claim 13 , wherein the link comprises a Uniform Resource Locator (URL).
15. The computing device of claim 13 , wherein the features of the link comprise at least one of a domain name, a subdomain name, a path, a symbol, a keyword, a document title and a meta description.
16. The computing device of claim 13 , wherein at least some of the compared features are brand-specific and at least some of the compared features are not brand-specific.
17. The computing device of claim 13 , further comprising processing logic for building the multi-dimensional input vector from the features of the link and from content of the webpage pointed to by the link.
18. The computing device of claim 13 , wherein the phishing probability engine comprises a Support Vector Machine (SVM) classifier.
19. The computing device of claim 18 , further comprising processing logic for training the SVM classifier with a corpus of non-phishing elements and phishing elements to generate a SVM model that is configured to be used by the phishing probability engine.
20. The computing device of claim 19 , wherein the corpus comprises elements that comprise a first file that comprises a hash of the link and a selected quantity and a second file that comprises a hash of contents of a webpage pointed to by the link and the selected quantity.
21. The computing device of claim 20 , wherein the hash comprises a message digest algorithm.
22. The computing device of claim 13 , further comprising processing logic for attempting to identify a brand that is a focus of at least one of the received email and the link.
23. The computing device of claim 13 , wherein the remote database stores a plurality of records, each of which comprising a logical construct that defines a brand.
24. The computing device of claim 23 , wherein the logical construct is formatted as a Document Type Definition (DTD).