Abstract: The presently disclosed subject matter includes an apparatus that receives a dataset with values associated with different digital resources captured from a group of compute devices. The apparatus includes a feature extractor, to generate a set of feature vectors, each feature vector from the set of feature vectors associated with a set of data included in the received dataset. The apparatus uses the set of feature vectors to validate multiple machine learning models trained to determine whether a digital resource is associated with a cyberattack. The apparatus selects at least one active machine learning model and sets the remaining trained machine learning models to operate in an inactive mode. The active machine learning model generates a signal to alert a security administrator, blocks a digital resource from loading at a compute device, or executes other remedial action, upon a determination that the digital resource is associated with a cyberattack.

Abstract: A system for protecting public cloud-hosted virtual resources features cloud visibility logic. According to one embodiment, the cloud visibility logic includes credential evaluation logic, data collection logic, correlation logic, and reporting logic. The credential evaluation logic is configured to gain authorized access to a cloud account within a first public cloud network. The data collection logic is configured to retrieve account data from the cloud account, while the correlation logic is configured to conduct analytics on the account data to determine whether the cloud account is subject to a cybersecurity threat or misconfiguration. The reporting logic is configured to generate an alert when the cloud account is determined by the correlation logic to be subject to the cybersecurity threat or misconfiguration.

Abstract: A device for verifying previous determinations from cybersecurity devices comprising a processor and a storage device communicatively coupled to the processor. The storage device comprises submission analysis logic including object parsing logic to receive submission message data and then parse the submission message data into object data, along with workflow selector logic to receive the object data and process the object data to select at least one analyzer within analyzer logic. The analyzer logic can generate at least one analyzer based on the selected analyzer within the workflow selector logic, analyze the object data for potential threats and embedded object data, generate results data based on that analysis, and pass the embedded object data back to the workflow selector for further analysis. Finally, the submission analysis logic comprises triage ticket generation logic to generate triage tickets for analyst review and alert logic to generate automatic alerts.

Abstract: A system for protecting public cloud-hosted virtual resources features cloud visibility logic. According to one embodiment, the cloud visibility logic includes credential evaluation logic, data collection logic, correlation logic and reporting logic. The credential evaluation logic is configured to gain authorized access to a cloud account within a first public cloud network. The data collection logic is configured to retrieve account data from the cloud account, while the correlation logic is configured to conduct analytics on the account data to determine whether the cloud account is subject to a cybersecurity threat or misconfiguration. The reporting logic is configured to generate an alert when the cloud account is determined by the correlation logic to be subject to the cybersecurity threat or misconfiguration.

Abstract: The presently disclosed subject matter includes an apparatus that receives a dataset with values associated with different digital resources captured from a group of compute devices. The apparatus includes a feature extractor, to generate a set of feature vectors, each feature vector from the set of feature vectors associated with a set of data included in the received dataset. The apparatus uses the set of feature vectors to validate multiple machine learning models trained to determine whether a digital resource is associated with a cyberattack. The apparatus selects at least one active machine learning model and sets the remaining trained machine learning models to operate in an inactive mode. The active machine learning model generates a signal to alert a security administrator, blocks a digital resource from loading at a compute device, or executes other remedial action, upon a determination that the digital resource is associated with a cyberattack.