Bitdefender Ipr Management Ltd.

Andreea Dincu

Daniel Dichiu

Elena A Bosinceanu

Sorina N Stoian

Andrei A Apostoae

Stefan Niculae

Robert M Botarleanu

Razvan Prejbeanu

Sorina N Zamfir

In some embodiments, a behavior classifier comprises a set of neural networks trained to determine whether a monitored software entity is malicious according to a sequence of computing events caused by the execution of the respective entity. When the behavior classifier indicates that the entity is malicious, some embodiments execute a memory classifier comprising another set of neural networks trained to determine whether the monitored entity is malicious according to a memory snapshot of the monitored entity. Applying the classifiers in sequence may substantially reduce the false positive detection rate, while reducing computational costs.

Machine learning systems and methods for reducing the false positive malware detection rate

In some embodiments, a behavioral computer security system protects clients and networks against threats such as malicious software and intrusion. A set of client profiles is constructed according to a training corpus of events occurring on clients, wherein each client profile represents a subset of protected machines, and each client profile is indicative of a normal or baseline pattern of using the machines assigned to the client respective profile. A client profile may group together machines having a similar event statistic. Following training, events detected on a client are selectively analyzed against a client profile associated with the respective client, to detect anomalous behavior. In some embodiments, individual events are analyzed in the context of other events, using a multi-dimensional event embedding space.

Systems and methods for behavioral threat detection

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Bucharest, Romania

Andreea Dincu