Abstract: A system for generating a lexicon of words, organized into weighted categories, from a user defined set of example documents for detecting suspicious e-mails from a mail archive is provided. The system uses a set of example documents and e-mails given by the user to probabilistically find possible lists of critical words. The obtained list is now applied on an archive of e-mails. The system generates an inverted index on the mails from the archive to facilitate search for the key phrases. User feedback is taken on the results obtained and corrections in the lexicon made if necessary. Thus, the mails are scanned based on user feedback, user defined words and automatically generated word list. These lists constantly adapt as e-mails in the archive change. The system then combines all these to present the user with several possible sets of keywords and their relative importance that can be used as a policy for a desired level of accuracy. The system also shows the user any change if the set is modified.

Abstract: A system for detecting information leakage in e-mails using neural network and support vector machines is provided. This system does not use the content of the e-mail or the content of the attachments in the e-mail. Instead, a set of non-sensitive variables or attributes is picked from the e-mails originating from a given establishment and also from the profiles of the users sending those mails. The said attributes are extracted for all outbound mails. This extraction process does not involve reading the main text of the mail and thus the sensitivity of the mail information is protected. These attributes are chosen using filters built into the detection hardware. Neural networks and support vector machine built into the detection hardware are then used on these attributes to detect pattern violation and possible information leakage.

Abstract: A system for generating a lexicon of words, organized into weighted categories, from a user defined set of example documents for detecting suspicious e-mails from a mail archive is provided. The system uses a set of example documents and e-mails given by the user to probabilistically find possible lists of critical words. The obtained list is now applied on an archive of e-mails. The system generates an inverted index on the mails from the archive to facilitate search for the key phrases. User feedback is taken on the results obtained and corrections in the lexicon made if necessary. Thus, the mails are scanned based on user feedback, user defined words and automatically generated word list. These lists constantly adapt as e-mails in the archive change. The system then combines all these to present the user with several possible sets of keywords and their relative importance that can be used as a policy for a desired level of accuracy. The system also shows the user any change if the set is modified.

Abstract: A system for detecting information leakage in e-mails using neural network and support vector machines is provided. This system does not use the content of the e-mail or the content of the attachments in the e-mail. Instead, a set of non-sensitive variables or attributes is picked from the e-mails originating from a given establishment and also from the profiles of the users sending those mails. The said attributes are extracted for all outbound mails. This extraction process does not involve reading the main text of the mail and thus the sensitivity of the mail information is protected. These attributes are chosen using filters built into the detection hardware. Neural networks and support vector machine built into the detection hardware are then used on these attributes to detect pattern violation and possible information leakage.