Threat detection and management
OwnerGanesh B. Dadi, Gridhar Duggirala +4inventors
InventorsGanesh B. Dadi, Gridhar Duggirala, Rameshchandra Bhaskar Ketharaju, Sathish Parameshwara, Venkata Praveen Potru, Suresh Reddy
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Abstract
Detecting and managing threats that arise from use of electronically-provided financial services. Large language models (LLMs) are trained to classify input data as different types of threats. In some examples, an LLM is trained on synthetic data generated by a generative adversarial network (GAN). Once a threat has been detected and classified, remedial measures can be automatically initiated.
Claims20
A method of managing threats to electronically-provided financial services, comprising:
- providing initial training data to a discriminator of a generative adversarial network (GAN), the initial training data being generated from a misuse of an electronically-provided financial service;
- training the discriminator using a generator of the GAN to provide a trained GAN;
- generating synthetic data using the trained GAN;
- and
- training a large language model (LLM) using the synthetic data to provide a trained LLM.
A system for managing threats to electronically-provided financial services, comprising:
- an electronic computing device, including:
- a financial services application installed on the electronic computing device and configured to initiate a transaction;
- and
- a threat detection module installed on the electronic computing device and configured as a plug-in to the financial services application, the threat detection module being configured to collect data associated with the transaction to provide collected data;
- and
- a server, including:
- one or more processors;
- and
- non-transitory computer-readable storage media encoding instructions which, when executed by the one or more processors, causes the system to:
- receive transaction data generated by the financial services application;
- receive the collected data;
- determine whether the collected data is indicative of a threat to an electronically-provided financial service;
- when the collected data is not indicative of the threat, allow the transaction to proceed;
- when the collected data is indicative of the threat:
- classify the threat using a large language model (LLM) to provide a classified threat;
- determine a remedial action based on a type of the classified threat;
- and
- perform the remedial action to address the threat based on the type.
A system for managing threats to electronically-provided financial services, comprising:
- one or more processors;
- and
- non-transitory computer-readable storage media encoding instructions which, when executed by the one or more processors, causes the system to:
- receive initial training data in a discriminator of a generative adversarial network (GAN), the initial training data being generated from a misuse of an electronically-provided financial service and including a screenshot captured by a screen scraper installed on an electronic computing device;
- training the discriminator using a generator of the GAN to provide a trained GAN;
- generate synthetic data using the trained GAN;
- train a large language model (LLM) using the synthetic data to provide a trained LLM;
- receive transaction data generated by a financial services application installed on another electronic computing device;
- receive, by the trained LLM, collected data collected by a threat detection module installed as a plug-in to the financial services application;
- classify, by the trained LLM and based on the collected data, a threat to provide a classified threat;
- and
- perform a remedial action to address the classified threat based on a type of the classified threat.
Dependencies are inferred from the claim text.