106 results about "Threat mitigation" patented technology

A method is provided for obtaining a certificate revocation list (CRL) for a vehicle in a vehicle-to-vehicle communication system. A portable security unit is provided to access secured operations for the vehicle. The portable security unit is linked to a device having access to a communication network. The communication network is in communication with a certificate authority for issuing an updated CRL. The updated CRL is downloaded from the certificate authority to the portable security unit. At a later time, when a user enters the vehicle, a communication link is established between the portable security unit and a vehicle processor unit. Mutual authentication is exchanged between the portable security unit and the vehicle processing unit. The updated CRL stored in the portable security unit is downloaded to a memory of the vehicle communication system in response to a successful mutual authentication.

The invention relates to predictive malware threat mitigation. The subject disclosure is directed towards protecting against malware, by classifying a user's risk level, which corresponds to a likelihood of malware being activated. To make the classification, data is collected that represents a probability of encountering malware, a probability of a user activating that malware, and the impact to the machine is activated. The classification maps to a protection level, which may be dynamically adjustable, e.g., based upon current risk conditions. The protection level determines a way to mitigate possible damage, such as by running a program as a virtualized program, running a virtualized operating system, or sandboxing a process.

Methods, systems and apparatus, including computer programs encoded on a computer storage medium, for assessing and managing cyber threats. In some implementations, data specifying relationships between I.T. system infrastructures, system categories, operational processes, computer-based threats and mitigation actions is received. A plurality of simulations are performed using a Monte Carlo method, with each simulation involving propagating data through stochastic modeling for a given time window having a beginning and end. Outcomes of the plurality of simulations that include mitigating actions representing the threat mitigation measures of the organization, for a given time window, determine a measure of impact of cyber threats to the organization. The determined measure is provided for output to a user.

According to various illustrative embodiments of the present invention, a method for a content-driven threat management system includes creating a threat detection primary object with threat mitigation data using at least one researcher of at least one of software and malicious code, encoding the threat detection primary object with threat mitigation data within an object capable of being transmitted to a personal computer system using a transmission vector update function, and transmitting the threat detection primary object with threat mitigation data to a threat agent using the transmission vector update function. The method also includes updating at least one threat function using the threat agent to update at least one threat functional description of the at least one threat function to include the threat detection primary object with threat mitigation data, and using the threat detection primary object with threat mitigation data to be capable of recognizing at least one threat event based upon the threat detection primary object with threat mitigation data.

A machine learning-based system and method for content clustering and content threat assessment includes generating embedding values for each piece of content of corpora of content data; implementing unsupervised machine learning models that: receive model input comprising the embeddings values of each piece of content of the corpora of content data; and predict distinct clusters of content data based on the embeddings values of the corpora of content data; assessing the distinct clusters of content data; associating metadata with each piece of content defining a member in each of the distinct clusters of content data based on the assessment, wherein the associating the metadata includes attributing to each piece of content within the clusters of content data a classification label of one of digital abuse / digital fraud and not digital abuse / digital fraud; and identifying members or content clusters having digital fraud / digital abuse based on querying the distinct clusters of content data.