Nvidia Corporation

Christopher Schneider

Andrew James Woodard

Benjemin Thomas Waine

Amy L Rose

Mark Alan Overby

Daniel Cory Rohrer

William Bartig

Alexander Matrosov

Amy Leigh Schneider Rose

Daniel Rohrer

A code analyzer implements machine learning to detect vulnerabilities in computer code. The code analyzer trains a machine learning model using training vectors that characterize vulnerable programming patterns. The code analyzer evaluates a topological representation of the computer code using the machine learning model to identify a potential vulnerability. The potential vulnerability corresponds to a portion of the computer code where an attack can be used to bypass a security procedure. The code analyzer tests the potential vulnerability by emulating a fault injection during execution of the portion of the computer code. Upon confirming that the potential vulnerability can be exploited via a fault injection, the code analyzer generates a training vector that characterizes a vulnerable programming pattern associated with the portion of the computer code. The training vector can be used to further train the machine learning model.

Detecting vulnerabilities to fault injection in computer code using machine learning

Apparatuses, systems, and techniques to detect abnormal behavior on clients using one or more neural networks on said clients. In at least one embodiment, use of or behavior on one or more clients is analyzed by a first neural network to detect abnormal behavior compared to a baseline of accepted behavior, and said baseline of accepted behavior is revised over time by a second neural network based on behavior observed on said one or more clients.

Behavior modeling using client-hosted neural networks

In various examples, a user may be authenticated without disclosing any confidential or private information of the user. An independent accumulator stores user confidential information, and accumulates items issued by various parties for the user. When another entity requests to verify the item and the user, the accumulator may verify the user by verifying his or her possession of the item and his or her private information. The accumulator may also verify the item with the issuing party and verify that the item was intended for the user. Once verification has occurred, the accumulator informs the requesting entity that their request is confirmed. In this manner, entities may verify items of a user, without requiring the user to disclose any of his or her confidential or private information to the requestor.

Conducting multi-party authentication in a privacy preserving manner

Apparatuses, systems, and techniques to accelerate training of one or more neural networks. In at least one embodiment, training values for individual nodes in a first neural network are estimated using a second neural network or other estimation technique in order to decrease time required to train the first neural network.

Accelerated training for neural network models

Statistical analysis can be used to attempt to identify potentially malicious references, such as trap URLs. When a URL is utilized for a request, that request can be intercepted before analysis before that URL is resolved to an address. Portions of this URL, as well as the entire URL, can be compared against one or more lists of known URLs using a probabilistic matching process to determine whether there are any matches that are very close but not quite exact. Any determined match with high probability above a suspicion threshold can be flagged as being suspicious, or associated with a potentially malicious site. An action can then be taken, such as to block that URL or prompt a user for confirmation of intent.

Detecting malformed resource references

In various examples, communications having insecure protocols are dynamically hardened. For example, communications that are formatted in an outdated or otherwise insecure version of a protocol (e.g., sent by a device aged out of a service window) may be isolated within a network, converted to an updated protocol format, or any combination thereof. These systems and methods may be implemented on a general purpose network device (e.g., a hub of a Local Area Network (LAN)).

Dynamically hardening communications having insecure protocols

In various examples, firewalls may include machine learning models that are automatically trained and applied to analyze service inputs submitted to input processing services and to identify whether service inputs are desirable (e.g., will result in an undesirable status code if processed by a service). When a service input is determined by a firewall to be desirable, the firewall may push the service input through to the input processing service for normal processing. When a service input is determined by the firewall to be undesirable, the firewall may block or drop the service input before it reaches the input processing service and/or server. This may be used to prevent the service input, which is likely to be undesirable, from touching a server that hosts the input processing service (e.g., preventing a crash).

Growing community of inventors

Fife, United Kingdom

Christopher Schneider