Abstract: Techniques for ranking a set of vulnerabilities of a computing asset and set of remediations for a computing asset, and determining a risk score for one or more computing assets are provided. In one technique, vulnerabilities of computing assets in a customer network are received at a vulnerability intelligence platform. Breach data indicating set of breaches that occurred outside customer network is also received. A subset of the set of vulnerabilities that are most vulnerable to a breach is identified based on the breach data. In another technique, multiple vulnerabilities of a computing asset are determined. A risk score is generated for the computing asset based on the vulnerabilities. In another technique, multiple remediations associated with a risk score and multiple vulnerabilities are identified. The remediations are ordered based on the remediations that would reduce the risk score the most if those remediations were applied to remove the corresponding vulnerabilities.

Abstract: Presented here are system and methods for collecting information from devices, such as sensors, that are not necessarily connected to the Internet. Multiple sensors are distributed in a geographic area. The sensors power up every 10 minutes to gather data about the environment and then power down to save battery. A collecting device, i.e., a device attached to a moving object, traverses the geographic area containing the sensors, and continuously sends wake-up signals into the environment. When a sensor is within 20 feet of the collecting device, and receives the wake-up signal, the sensor uploads the gathered data to the collecting device. Subsequently, when the collecting device establishes an Internet connection, the collecting device uploads the gathered data to a central database.

Abstract: Presented here are system and methods for collecting information from devices, such as sensors, that are not necessarily connected to the Internet. Multiple sensors are distributed in a geographic area. The sensors power up every 10 minutes to gather data about the environment and then power down to save battery. A collecting device, i.e., a device attached to a moving object, traverses the geographic area containing the sensors, and continuously sends wake-up signals into the environment. When a sensor is within 20 feet of the collecting device, and receives the wake-up signal, the sensor uploads the gathered data to the collecting device. Subsequently, when the collecting device establishes an Internet connection, the collecting device uploads the gathered data to a central database.

Abstract: Presented here is a system for automatic conversion of data between various data sets. In one embodiment, the system can obtain a data set, can analyze associations between the variables in the data set, and can convert the data set into a canonical data model. The canonical data model is a smaller representation of the original data set because insignificant variables and associations can be left out, and significant relationships can be represented procedurally and/or using mathematical functions. In one embodiment, part of the system can be a trained machine learning model which can convert the input data set into a canonical data model. The canonical data model can be a more efficient representation of the input data set. Consequently, various actions, such as an analysis of the data set, merging of two data sets, etc. can be performed more efficiently on the canonical data model.

Abstract: Presented here is a system for automatic conversion of data between various data sets. In one embodiment, the system can obtain a data set, can analyze associations between the variables in the data set, and can convert the data set into a canonical data model. The canonical data model is a smaller representation of the original data set because insignificant variables and associations can be left out, and significant relationships can be represented procedurally and/or using mathematical functions. In one embodiment, part of the system can be a trained machine learning model which can convert the input data set into a canonical data model. The canonical data model can be a more efficient representation of the input data set. Consequently, various actions, such as an analysis of the data set, merging of two data sets, etc. can be performed more efficiently on the canonical data model.

Abstract: Generation of one or more models is caused based on selecting training data comprising a plurality of features including a prevalence feature for each vulnerability of a first plurality of vulnerabilities. The one or more models enable predicting whether an exploit will be developed for a vulnerability and/or whether the exploit will be used in an attack. The one or more models are applied to input data comprising the prevalence feature for each vulnerability of a second plurality of vulnerabilities. Based on the application of the one or more models to the input data, output data is received. The output data indicates a prediction of whether an exploit will be developed for each vulnerability of the second plurality. Additionally or alternatively, the output data indicates, for each vulnerability of the second plurality, a prediction of whether an exploit that has yet to be developed will be used in an attack.

Abstract: Techniques for ranking a set of vulnerabilities of a computing asset and set of remediations for a computing asset, and determining a risk score for one or more computing assets are provided. In one technique, vulnerabilities of computing assets in a customer network are received at a vulnerability intelligence platform. Breach data indicating set of breaches that occurred outside customer network is also received. A subset of the set of vulnerabilities that are most vulnerable to a breach is identified based on the breach data. In another technique, multiple vulnerabilities of a computing asset are determined. A risk score is generated for the computing asset based on the vulnerabilities. In another technique, multiple remediations associated with a risk score and multiple vulnerabilities are identified. The remediations are ordered based on the remediations that would reduce the risk score the most if those remediations were applied to remove the corresponding vulnerabilities.

Abstract: Techniques for ranking a set of vulnerabilities of a computing asset and set of remediations for a computing asset, and determining a risk score for one or more computing assets are provided. In one technique, vulnerabilities of computing assets in a customer network are received at a vulnerability intelligence platform. Breach data indicating set of breaches that occurred outside customer network is also received. A subset of the set of vulnerabilities that are most vulnerable to a breach is identified based on the breach data. In another technique, multiple vulnerabilities of a computing asset are determined. A risk score is generated for the computing asset based on the vulnerabilities. In another technique, multiple remediations associated with a risk score and multiple vulnerabilities are identified. The remediations are ordered based on the remediations that would reduce the risk score the most if those remediations were applied to remove the corresponding vulnerabilities.

Abstract: The technology presented here enables low skilled administrators to design a hierarchical survey, low skilled field agents to collect answers to the hierarchical survey, and low skilled field managers to manage and monitor the progress of the field agents. The hierarchical surveys designed can be complex hierarchical surveys comprising multi-stage sampling units. The graphical user interfaces presented to the users are easy to use, and hide the complexity of the hierarchical survey. The user devices can communicate with each other to transmit the hierarchical surveys and the answers received to the hierarchical surveys using peer-to-peer networks, in environments where there is low, or no Internet connectivity.

Abstract: Techniques for ranking a set of vulnerabilities of a computing asset and set of remediations for a computing asset, and determining a risk score for one or more computing assets are provided. In one technique, vulnerabilities of computing assets in a customer network are received at a vulnerability intelligence platform. Breach data indicating set of breaches that occurred outside customer network is also received. A subset of the set of vulnerabilities that are most vulnerable to a breach is identified based on the breach data. In another technique, multiple vulnerabilities of a computing asset are determined. A risk score is generated for the computing asset based on the vulnerabilities. In another technique, multiple remediations associated with a risk score and multiple vulnerabilities are identified. The remediations are ordered based on the remediations that would reduce the risk score the most if those remediations were applied to remove the corresponding vulnerabilities.

Abstract: Techniques for ranking a set of vulnerabilities of a computing asset and set of remediations for a computing asset, and determining a risk score for one or more computing assets are provided. In one technique, vulnerabilities of computing assets in a customer network are received at a vulnerability intelligence platform. Breach data indicating set of breaches that occurred outside customer network is also received. A subset of the set of vulnerabilities that are most vulnerable to a breach is identified based on the breach data. In another technique, multiple vulnerabilities of a computing asset are determined. A risk score is generated for the computing asset based on the vulnerabilities. In another technique, multiple remediations associated with a risk score and multiple vulnerabilities are identified. The remediations are ordered based on the remediations that would reduce the risk score the most if those remediations were applied to remove the corresponding vulnerabilities.

Abstract: Techniques for ranking a set of vulnerabilities of a computing asset and set of remediations for a computing asset, and determining a risk score for one or more computing assets are provided. In one technique, vulnerabilities of computing assets in a customer network are received at a vulnerability intelligence platform. Breach data indicating set of breaches that occurred outside customer network is also received. A subset of the set of vulnerabilities that are most vulnerable to a breach is identified based on the breach data. In another technique, multiple vulnerabilities of a computing asset are determined. A risk score is generated for the computing asset based on the vulnerabilities. In another technique, multiple remediations associated with a risk score and multiple vulnerabilities are identified. The remediations are ordered based on the remediations that would reduce the risk score the most if those remediations were applied to remove the corresponding vulnerabilities.

Abstract: Techniques for ranking a set of vulnerabilities of a computing asset and set of remediations for a computing asset, and determining a risk score for one or more computing assets are provided. In one technique, vulnerabilities of computing assets in a customer network are received at a vulnerability intelligence platform. Breach data indicating set of breaches that occurred outside customer network is also received. A subset of the set of vulnerabilities that are most vulnerable to a breach is identified based on the breach data. In another technique, multiple vulnerabilities of a computing asset are determined. A risk score is generated for the computing asset based on the vulnerabilities. In another technique, multiple remediations associated with a risk score and multiple vulnerabilities are identified. The remediations are ordered based on the remediations that would reduce the risk score the most if those remediations were applied to remove the corresponding vulnerabilities.

Abstract: Techniques for ranking a set of vulnerabilities of a computing asset and set of remediations for a computing asset, and determining a risk score for one or more computing assets are provided. In one technique, vulnerabilities of computing assets in a customer network are received at a vulnerability intelligence platform. Breach data indicating set of breaches that occurred outside customer network is also received. A subset of the set of vulnerabilities that are most vulnerable to a breach is identified based on the breach data. In another technique, multiple vulnerabilities of a computing asset are determined. A risk score is generated for the computing asset based on the vulnerabilities. In another technique, multiple remediations associated with a risk score and multiple vulnerabilities are identified. The remediations are ordered based on the remediations that would reduce the risk score the most if those remediations were applied to remove the corresponding vulnerabilities.

Abstract: Method and system for allowing a computer network operations manager to subscribe for and receive notifications concerning network events from one or more objects or object levels, as defined by distinguished name scoping, and optionally having at least one event characteristic from a selected list. The selected list of characteristics may include: one or more levels of network objects involved in the event; one or more specified network nodes involved in the event; a specified geographical region in which said event occurs; a specified period of days within which the event occurs or is initiated; a specified time interval within which the event occurs or is initiated; a specified class of devices involved in the event; and an event of one or more specified event types.

Abstract: The alarm manager display in a distributed network management system is arranged to have two modes of operation. In one mode of operation, the alarm manager display automatically scrolls when new events arrive. If there are sorting criteria defined, the alarm manager window scrolls either up or down depending on the sort order so that when new events arrive, they always appear on the screen. In the second mode of operation, the alarm manager window does not scroll when new events arrive. The scroll bar operational modes are selectable by an operator from a menu. In accordance with another embodiment, a special attribute is added to the alarm manager configuration file. This attribute is read when the alarm manager is started and places the alarm manager into the operational mode in which it was last used.

Abstract: The alarm manager display in a distributed network management system is arranged to have two modes of operation. In one mode of operation, the alarm manager display automatically scrolls when new events arrive. If there are sorting criteria defined, the alarm manager window scrolls either up or down depending on the sort order so that when new events arrive, they always appear on the screen. In the second mode of operation, the alarm manager window does not scroll when new events arrive. The scroll bar operational modes are selectable by an operator from a menu. In accordance with another embodiment, a special attribute is added to the alarm manager configuration file. This attribute is read when the alarm manager is started and places the alarm manager into the operational mode in which it was last used.