Fireeye, Inc.

Musarubra US LLC

Robert Venal

Jijo Xavier

Wei Quan

Chunsheng Fang

Richard Lai

Benjamin Chang

According to one embodiment, a system for detecting an email campaign includes feature extraction logic, pre-processing logic, campaign analysis logic and a reporting engine. The feature extraction logic obtains features from each of a plurality of malicious email messages received for analysis while the pre-processing logic generates a plurality of email representations that are arranged in an ordered sequence and correspond to the plurality of malicious email message. The campaign analysis logic determines the presence of an email campaign in response to a prescribed number of successive email representations being correlated to each other, where the results of the email campaign detection are provided to a security administrator via the reporting engine.

System and method for detecting repetitive cybersecurity attacks constituting an email campaign

A method for generating rule recommendation utilized in a creation of malware detection rules is described. Meta-information associated with a plurality of events collected during a malware detection analysis of an object by a cybersecurity system is received and a first plurality of features is selected from the received meta-information. Machine learning (ML) models are applied to each of the first plurality of features to generate a score that represents a level of maliciousness for the feature and thereby a degree of usefulness of the feature in classifying the object as malicious or benign. Thereafter, a second plurality of features is selected as the salient features, which are used in creation of the malware detection rules in controlling subsequent operations of the cybersecurity system. The second plurality of features being lesser in number that the first plurality of features.

System and method for automatically generating malware detection rule recommendations

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Milpitas, CA, United States of America

Robert Venal