Rapid7, Inc.

Wah-Kwan Lin

Roy Donald Hodgman

Vasudha Shivamoggi

Paul Deardorff

Leonardo Varela Guevara

Dustin Myers

Oliver Keyes

Deral Heiland

Cody R Pierce

Michael Scutt

Timothy Stiller

Curtis Barnard

Harley Ray Rogers

Cody Pierce

Methods and systems for identifying a vulnerability on a network are disclosed. The methods described herein may involve executing a first scanning function to obtain a first view of a network and then filtering the first view of the network for at least one point of exposure of a first entity that originates from a second entity. The methods described herein may further involve executing a secondary scanning function to identify any vulnerabilities of the first entity based on the point of exposure of the first entity that originates from the second entity and implementing a threat prevention procedure upon identifying a vulnerability of the first entity based on the point of exposure of the first entity that originates from the second entity.

Vulnerability detection at points of exposure

A server determines vulnerabilities associated with components of a computing device. The server determines attributes associated with individual vulnerabilities. The server determines a subset of the vulnerabilities that includes unexploited vulnerabilities. The server executes a machine learning model to predict a probability of an exploit being created for a particular unexploited vulnerability in the subset. The server sends to a device: information identifying the particular unexploited vulnerability, particular attributes associated with the particular unexploited vulnerability, and the probability of an exploit being created for the particular unexploited vulnerability.

Predicting a probability associated with an unexploited vulnerability

Systems and methods are provided to build a machine learned exploitability risk model that predicts, based on the characteristics of a set of machines, a normalized risk score quantifying the risk that the machines are exploitable by a set of attacks. To build the model, a training dataset is constructed by labeling characteristic data of a population of machines with exploitation test results obtained by simulating a set of attacks on the population. The model is trained using the training data to accurately predict a probability that a given set of machines is exploitable by the set of attacks. In embodiments, the model may be used to make quick assessments about how vulnerable a set of machines are to the set of attacks. In embodiments, the model may be used to compare the effectiveness of different remediation actions to protect against the set of attacks.

Recommendation interface for remediation plans for cyberattack vulnerabilities

Various embodiments include systems and methods of assessing vendor risk. One or more sets of IP address(es) associated with one or more vendors is identified. Risk data related to the set(s) of IP address(es) is obtained using internet telemetry data. Based at least in part on the risk data, security risk level(s) are determined for the vendor(s). Some embodiments include systems and methods of implementing a vendor-based risk posture assessment of an organization. The vendor-based risk posture assessment may be based at least in part on one or more security risk levels determined for the vendor(s) of the organization.

Vendor risk assessment using internet telemetry

Disclosed herein are methods, systems, and processes for probabilistically identifying anomalous levels of honeypot activity. A honeypot dataset associated with a honeypot network is received and a representative usage value is determined from the honeypot dataset. The representative usage value is identified as being associated with anomalous behavior if the representative usage value deviates from an expected probability distribution. A remediation operation is initiated in the honeypot network in response to the identification of the representative usage value as being associated with the anomalous behavior by virtue of the representative usage value deviating from the expected probability distribution.

Honeypot network management based on probabilistic detection of malicious port activity

Various embodiments include systems and methods to implement a graph analysis-based assessment to determine relative node significance. Network traffic data associated with a network may be obtained. A graph analysis-based assessment of the network may be performed to determine network traffic paths between a plurality of nodes in the network based at least in part on the network traffic data and to calculate, for each node and based at least in part on the network traffic paths, a respective centrality value. The respective centrality value may be indicative of a respective node being a potential source of disruption to the network relative to other nodes. At least one significant node in the network may be identified based at least in part on the centrality values, and a particular action to be performed with respect to the at least one significant node may be determined.

Graph analysis-based assessment to determine relative node significance

Disclosed herein are methods, systems, processes, and machine learned models for performing opinionated threat assessments for cybersecurity vulnerabilities. An opinionated threat assessment system is implemented that obtains a training dataset that includes a codified opinionated threat assessment for security vulnerabilities. The codified opinionated threat assessment in the training dataset includes intrinsic attributes for the security vulnerabilities and subject attributes about the security vulnerabilities. The opinionated threat assessment system trains an opinionated threat assessment model using the training dataset and according to a machine learning technique where the training tunes the opinionated threat assessment model to generate a machined learned opinionated threat assessment for a new security vulnerability based on new intrinsic attributes associated with the new security vulnerability.

Machine learned model for generating opinionated threat assessments of security vulnerabilities

Methods and systems for training a language processing model. The methods may involve receiving a first log record in a first format, wherein the first log record includes annotations describing items in the first log record, and then creating a second log record in a second format comprising data from the first log record utilizing the annotations in the first log record and a conversion rule set. The second log record may then be used to train a language processing model so that a trained model can identify items in a third log record and the relationships therebetween.

Threat detection system that reformats log records for NLP model-based threat detection packages

Methods and systems for identifying a network threat are disclosed. The methods described herein may involve receiving at least one permutation of a domain name, wherein the at least one permutation is registered with a domain name registrar. The methods described herein may further involve executing a scanning function to identify an active service on the at least one permutation registered with the domain name registrar and implementing a threat prevention procedure upon identifying an active service on the at least one permutation.

Network threat prevention

Probabilistically identifying anomalous honeypot activity

Disclosed herein are methods, systems, and processes to detect valid clusters and eliminate spurious clusters in cybersecurity-based computing environments. A cluster detection and elimination model is trained by accessing a dataset with raw data that includes data points associated with computing devices in a network and applying two or more different clustering methodologies independently to the dataset. The resulting cluster detection and elimination model is used to compare two or more clusters to determine whether a cluster from one clustering methodology matches another cluster from another clustering methodology based on centroid locations and shared data points.

Cluster-based scheduling of security operations

Methods and systems for classifying a device on a network. The systems and methods may receive network activity data associated with an unknown device. A classifier executing one or more machine learning models may then classify the device as an internet of things (IoT) device or a non-IoT device.

Autonomous application of security measures to IoT devices

Disclosed herein are methods, systems, and processes to optimize role level identification for computing resource allocation to perform security operations in networked computing environments. A role level classifier to process a training dataset that corresponds to a clean title is generated from a subset of entities associated with the clean title. An initial effective title determined by the role level classifier based on processing the training dataset is assigned to an entity. A new effective title based on feature differences between the initial effective title and the clean title is re-assigned to the entity. Performance of the generating, the assigning, and the re-assigning is repeated using the new effective title instead of the clean title.

Optimizing role level identification for resource allocation

Analyzing and reporting anomalous internet traffic data by accepting a request for a connection to a virtual security appliance, collecting attribute data about the connection, applying an alert module to the data, and automatically generating an alert concerning an identified incident. An alert system for analyzing and reporting the anomalous internet traffic data. A processor to analyze and report anomalous internet traffic data.

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Melrose, MA, United States of America

Wah-Kwan Lin