Armis Security Ltd.

Systems and methods for device profile enrichment. A method includes determining a plurality of distributions of device attributes with respect to a plurality of fields of a predefined device profile schema; generating a plurality of inference rules based on the plurality of distributions of device attributes, wherein each inference rule indicates at least one required device attribute and at least one inferred device attribute; creating an ordered set of inference rules including the plurality of inference rules organized with respect to a plurality of scores, each score corresponding to one of the plurality of inference rules, wherein the score for each inference rule is determined based on the at least one required device attribute of the inference rule; and enriching at least one device profile by iterating the ordered set of inference rules, wherein enriching a device profile includes adding at least one device attribute value to the device profile.

A system and method for determining device attributes based on host configuration protocols. A method includes applying at least one machine learning model to a test data set extracted from host configuration protocol data including at least one test options sequence, wherein each test options sequence is an ordered series of options requested by a first device, wherein each of the at least one machine learning model is trained based on a train data set including a plurality of training options sequences and a plurality of device attributes, wherein each training options sequence and each device attribute of the train data set corresponds to a respective second device; and determining, based on the output of the at least one machine learning model, at least one device attribute for the first device.

This disclosure relates to systems, methods, and devices for identifying anomalous network activity. In some embodiments, a baseline model is used for identifying anomalous network activity. In some embodiments, anomalous network activity is detected based on a z-score, modified z-score, or both being above respective thresholds when compared to the baseline. In some embodiments, multiple baseline models are used, and anomalous network activity is detected when multiple baseline models identify a network activity session as anomalous. In some embodiments, two baseline models are used.

A system and method for identifying device attributes based on string field conventions. A method includes applying at least one machine learning model to an application data set extracted based on a string indicated in a field of device data corresponding to a device, wherein each of the at least one machine learning model is trained based on a training data set including a plurality of second strings and a plurality of device attribute labels, wherein each device attribute label corresponds to a respective second string of the plurality of second strings, wherein each of the at least one machine learning model is configured to output a predicted device attribute for the device based on the first string; and identifying, based on the output of the at least one machine learning model, a device attribute of the device.

The present disclosure relates to systems and methods for determining comprehensive and asset vulnerability ratings using models such as artificial intelligence (AI) and machine learning (ML) models. These models can identify relevant attributes, optimize attribute values, and determine logical relationships between attributes. The term “model” encompasses various types of AI and ML models, including neural networks, language models, multimodal models, and others. Models can be trained using supervised learning with labeled data to predict or classify new data items. The models can be locally hosted, cloud-managed, or accessed via APIs, and can be implemented in electronic hardware such as computer processors.

A system and method for anomaly interpretation and mitigation. A method includes extracting at least one input feature vector from observation data related to an observation; applying an isolation forest to the at least one input feature vector, wherein the isolation forest includes a plurality of estimators, wherein each estimator is a decision tree, wherein the output of each estimator is a split-path of a plurality of split-paths, each split-path having a path-length and including name and a corresponding value for a respective output feature of a plurality of output features; generating a mapping object based on the application of the isolation forest to the at least one feature vector, wherein the mapping object includes the plurality of split-paths; clipping the mapping object based on the path-length of each split-path; and determining at least one mitigation action based on the clipped mapping object.

A system and method for identifying device attributes based on string field conventions. A method includes applying at least one machine learning model to an application data set extracted based on a string indicated in a field of device data corresponding to a device, wherein each of the at least one machine learning model is trained based on a training data set including a plurality of second strings and a plurality of device attribute labels, wherein each device attribute label corresponds to a respective second string of the plurality of second strings, wherein each of the at least one machine learning model is configured to output a predicted device attribute for the device based on the first string; and identifying, based on the output of the at least one machine learning model, a device attribute of the device.

Systems and methods for device profile enrichment. A method includes determining a plurality of distributions of device attributes with respect to a plurality of fields of a predefined device profile schema; generating a plurality of inference rules based on the plurality of distributions of device attributes, wherein each inference rule indicates at least one required device attribute and at least one inferred device attribute; creating an ordered set of inference rules including the plurality of inference rules organized with respect to a plurality of scores, each score corresponding to one of the plurality of inference rules, wherein the score for each inference rule is determined based on the at least one required device attribute of the inference rule; and enriching at least one device profile by iterating the ordered set of inference rules, wherein enriching a device profile includes adding at least one device attribute value to the device profile.

Systems and methods for device profile enrichment. A method includes determining a plurality of distributions of device attributes with respect to a plurality of fields of a predefined device profile schema; generating a plurality of inference rules based on the plurality of distributions of device attributes, wherein each inference rule indicates at least one required device attribute and at least one inferred device attribute; creating an ordered set of inference rules including the plurality of inference rules organized with respect to a plurality of scores, each score corresponding to one of the plurality of inference rules, wherein the score for each inference rule is determined based on the at least one required device attribute of the inference rule; and enriching at least one device profile by iterating the ordered set of inference rules, wherein enriching a device profile includes adding at least one device attribute value to the device profile.

A system and method for identifying device attributes based on string field conventions. A method includes applying at least one machine learning model to an application data set extracted based on a string indicated in a field of device data corresponding to a device, wherein each of the at least one machine learning model is trained based on a training data set including a plurality of second strings and a plurality of device attribute labels, wherein each device attribute label corresponds to a respective second string of the plurality of second strings, wherein each of the at least one machine learning model is configured to output a predicted device attribute for the device based on the first string; and identifying, based on the output of the at least one machine learning model, a device attribute of the device.