Ao Kaspersky Lab

Yury G Slobodyanuk

Dmitry S Golubev

Roman A Dedenok

Nikita D Benkovich

Alexey S Marchenko

Alexey E Utki-Otki

Daniil M Kovalchuk

A method for generating a signature of a spam message includes determining one or more classification attributes and one or more clustering attributes contained in successively intercepted first and second electronic messages. The first electronic message is classified using a trained classification model for classifying electronic messages based on the one or more classification attributes. The first electronic message is classified as spam if a degree of similarity of the first electronic message to one or more spam messages is greater than a predetermined value. A determination is made whether the first electronic message and the second electronic message belong to a single cluster based on the determined one or more clustering attributes. A signature of a spam message is generated based on the the identified single cluster of electronic messages.

System and method for creating a signature of a spam message

A method creating a heuristic rule to identify Business Email Compromise (BEC) attacks includes filtering text of received email messages, using a first classifier, to extract one or more terms indicative of a BEC attack from the text of the received email messages, wherein the first classifier includes a trained recurrent neural network that includes a language model, generating, using the first classifier, one or more n-grams based on the extracted terms, wherein each of the n-grams characterizes a particular extracted term, generating, using a second classifier, a vector representation of the extracted terms based on the generated n-grams, assigning a weight coefficient to each of the extracted terms, wherein a higher weight coefficient indicates higher relevancy to BEC attack of the corresponding extracted term, and generating a heuristic rule associated with the BEC attack by combining the weight coefficients of a combination of the extracted terms.

System and method for creating heuristic rules based on received email messages to identity business email compromise attacks

A method for restricting reception of e-mail messages from a sender of bulk spam mail includes identifying an unknown sender of received e-mail messages. A set of e-mail messages received from the identified sender is selected. A type of bulk spam mailing is determined based on the selected set of e-mail messages using one or more spam identification signatures. Restrictions on reception of e-mail messages from a sender distributing bulk spam of the determined type are generated.

System and method for restricting the reception of e-mails from a sender of bulk spam mail

A method for creating a heuristic rule to identify Business Email Compromise (BEC) attacks includes filtering text of received email messages, using a first classifier, to extract one or more terms indicative of a BEC attack from the text of the received email messages. One or more n-grams are generated, using the first classifier, based on the extracted terms. A vector representation of the extracted terms is generated, using a second classifier, based on the generated one or more n-grams. The second classifier includes a logit model. A weight coefficient is assigned to each of the one or more extracted terms based on an output of the trained logit model. A higher weight coefficient indicates higher relevancy to BEC attack of the corresponding term. A heuristic rule associated with the BEC attack is generated by combining the weight coefficients of a combination of the one or more extracted terms.

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Moscow, Russia

Yury G Slobodyanuk