Abnormal Security Corporation

Abnormal Ai, Inc.

Asapp, Inc.

Lei Xu

Jeshua Alexis Bratman

Yu Zhou Lee

Lawrence Stockton Moore

Rami Faris Habal

Sanjay Jeyakumar

Shawn Henry

Michael Griffiths

Introduced here are computer programs and computer-implemented techniques for generating and then managing a federated database that can be used to ascertain the risk in interacting with vendors. At a high level, the federated database allows knowledge regarding the reputation of vendors to be shared amongst different enterprises with which those vendors may interact. A threat detection platform may utilize the federated database when determining how to handle incoming emails from vendors.

Federated database for establishing and tracking risk of interactions with third parties

A generated training set comprising a plurality of training samples is received. The generated training set includes at least one training sample constructed using one or more linguistic hints, comprising at least one keyword of phrase, about an attack for which malicious textual communications associated with the attack, when processed by a natural language processing model could be classified as benign textual communications before being trained using the generated training set. The natural language processing model is trained at least in part by using the generated training set, wherein the trained natural language processing model is configured to determine a likelihood that a received communication transmitted by a sender to a recipient poses a risk.

Adaptive detection of security threats through training of computer-implemented models

Adapting detection of security threats, including by retraining computer-implemented models is disclosed. An indication is received that a natural language processing model should be retrained. A list of training samples is generated that includes at least one synthetic training sample. The natural language processing model is retrained at least in part by using the set of generated training samples. The retrained natural language processing model is used to determine a likelihood that a message poses a risk.

Adaptive detection of security threats through retraining of computer-implemented models

Techniques for detecting instances of external fraud by monitoring digital activities that are performed with accounts associated with an enterprise are disclosed. In one example, a threat detection platform determines the likelihood that an incoming email is indicative of external fraud based on the context and content of the incoming email. To understand the risk posed by an incoming email, the threat detection platform may seek to determine not only whether the sender normally communicates with the recipient, but also whether the topic is one normally discussed by the sender and recipient. In this way, the threat detection platform can establish whether the incoming email deviates from past emails exchanged between the sender and recipient.

Detection and prevention of external fraud

Estimating risk posed by interacting with third parties through analysis of emails addressed to employees of multiple enterprises

Approaches to creating, managing, and applying a federated database to establish risk posed by third parties

Introduced here are computer programs and computer-implemented techniques for detecting instances of external fraud by monitoring digital activities that are performed with accounts associated with an enterprise. A threat detection platform may determine the likelihood that an incoming email is indicative of external fraud based on the context and content of the incoming email. For example, to understand the risk posed by an incoming email, the threat detection platform may seek to determine not only whether the sender normally communicates with the recipient, but also whether the topic is one normally discussed by the sender and recipient. In this way, the threat detection platform can establish whether the incoming email deviates from past emails exchanged between the sender and recipient.

A plurality of conversations may be processed to obtain one or more representative conversations to allow a better understanding of the plurality of conversations. A representative conversation may be determined by representing each conversation as a sequence of states where a state may represent messages with similar meanings. Distances may be computed between pairs of conversations, and the conversations may be clustered using the distances. To obtain a representative conversation for a cluster of conversations, a representative sequence of states may be obtained for the cluster and a representative message may be obtained for each state of the sequence of states. The representative conversation may then be presented to a user.

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New York, NY, United States of America

Lei Xu