Narus, Inc.

Symantec Corporation

Ca, Inc.

Ignacio Bermudez Corrales

Antonio Nucci

Alok Tongaonkar

Aleatha Parker-Wood

Ryan Curtin

Reuben Feinman

The disclosed computer-implemented method for malware remediation may include constructing a malware detection model by (i) identifying multiple candidate hyperparameter sets, (ii) selecting, from the candidate hyperparameter sets, a set of hyperparameters for the malware detection model that optimizes a tradeoff between model efficacy and model size, and (iii) training the malware detection model on a set of training samples to distinguish between malicious samples and clean samples. After constructing the malware detection model, the disclosed computer-implemented method may also include using the constructed malware detection model to perform a security action. Various other methods, systems, and computer-readable media are also disclosed.

Optimizing a malware detection model using hyperparameters

A trusted user circle server for encryption key distribution and authentication support, as well as a client-side application which resides on user's devices are disclosed. In particular, the trusted user circle server manages a repository for static public keys (SPUK) which are used for authentication and secure distribution of a dynamic private context key (DPCK) used for the end-to-many encryption. Accordingly, posting users encrypt posted document using the DPCK and viewing users retrieve the DPCK to decrypt the posted document. These keys are associated to the trusted user circle and are generated dynamically for a given circle policy context (CPC). The CPC is an identifier that represents a group of members of a trusted user circle. It changes whenever any member of the trusted user circle leave it, when a new trusted user circle is created or when the DPCK expires after a pre-determined period of time.

Trusted user circles

The disclosed computer-implemented method for identifying compromised devices within industrial control systems may include (1) monitoring network traffic within a network that facilitates communication for an industrial control system that includes an industrial device, (2) creating, based at least in part on the network traffic, a message protocol profile for the industrial device that describes (A) a network protocol used to communicate with the industrial device and (B) normal communication patterns of the industrial device, (3) detecting at least one message that involves the industrial device and at least one other computing device included in the industrial control system, (4) determining, by comparing the message with the message protocol profile, that the message represents an anomaly, and then (5) determining, based at least in part on the message representing the anomaly, that the other computing device has likely been compromised. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for identifying compromised devices within industrial control systems

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Sunnyvale, CA, United States of America

Ignacio Bermudez Corrales