Abstract: Some embodiments provide techniques for generating common vulnerability scoring system (CVSS) vectors for vulnerabilities to use in scanning a computing environment for vulnerabilities. The techniques involve obtaining a textual description of a vulnerability; generating inputs for a plurality of ML models using the textual description of the vulnerability; providing the inputs to the plurality of ML models to obtain outputs indicating values of CVSS risk metrics; and storing the values of the CVSS risk metrics indicated by the outputs of the plurality of ML models in a vector to obtain the CVSS vector for the vulnerability.

Abstract: Machine learning techniques for updating a configuration of a computer network security system operating in a cloud computing environment. The techniques include obtaining a plurality of datasets containing information about a respective plurality of events detected by the computer network security system in the cloud computing environment; generating, using at least one trained ML model, a plurality of signatures representing the plurality of events, the generating comprising processing the plurality of datasets using the at least one trained ML model to obtain the plurality of signatures; clustering the plurality of signatures to obtain signature clusters representing clusters of events in the plurality of events; identifying a particular event cluster from among the clusters of events; and updating the configuration of the computer network security system based on characteristics of events in the identified particular event cluster.

Abstract: A method for classifying a digital certificate as malicious or non-malicious includes receiving the digital certificate from a network source and extracting textual fields from the certificate. The extracted text is embedded into a high-dimensional vector using a pretrained transformer-based encoder. The resulting test vector is queried against a vector data structure populated with reference vectors derived from known benign and malicious certificates. A similarity search is performed to identify a set of nearest reference vectors. A classification decision is made based on the labels of the most similar/nearest neighbors, using a voting mechanism. If a given set or number of them are labeled as malicious, the certificate is classified as malicious. If not, it is classified as benign. The classification result may trigger a network security action, such as blacklisting the associated IP address or identifying a botnet command and control server.

Abstract: Techniques for verifying correctness of associations between assets related to events detected in at least one computer network and assets in an asset catalog for the at least one computer network. The techniques include obtaining information specifying a first asset and a first set of assets with which the first asset was previously associated; generating a signature of the first asset from the computer network addressing information for the first asset; generating a hashed signature by applying a locality sensitive hashing (LSH) technique to the signature; associating the first asset with a second set of assets in the asset catalog using the hashed signature and at least one hashed signature of the at least one asset in the asset catalog; and when it is determined that the second set of includes the first set, outputting an indication that the first asset was correctly associated with the first set of assets.

Abstract: Techniques for associating assets related to events detected in at least one computer network with respective assets in an asset catalog for the at least one computer network. The techniques include: while monitoring activity on the at least one computer network, obtaining information about an event related to a first asset, the information specifying computer network addressing information for the first asset; generating a signature of the first asset from the computer network addressing information; generating a hashed signature of the first asset by applying a locality sensitive hashing (LSH) technique to the signature; associating the first asset with at least one asset in the asset catalog using the hashed signature of the first asset and at least one hashed signature of the at least one asset in the asset catalog; and outputting information identifying the at least one asset with which the first asset was associated.

Abstract: Machine learning techniques for updating a configuration of a computer network security system operating in a cloud computing environment. The techniques include obtaining a plurality of datasets containing information about a respective plurality of events detected by the computer network security system in the cloud computing environment; generating, using at least one trained ML model, a plurality of signatures representing the plurality of events, the generating comprising processing the plurality of datasets using the at least one trained ML model to obtain the plurality of signatures; clustering the plurality of signatures to obtain signature clusters representing clusters of events in the plurality of events; identifying a particular event cluster from among the clusters of events; and updating the configuration of the computer network security system based on characteristics of events in the identified particular event cluster.

Abstract: Techniques for verifying correctness of associations between assets related to events detected in at least one computer network and assets in an asset catalog for the at least one computer network. The techniques include: obtaining information specifying a first asset and a first set of assets with which the first asset was previously associated; generating a signature of the first asset from computer network addressing information for the first asset using at least one trained machine learning model; associating the first asset with a second set of assets using the signature and at least one signature of the at least one asset, wherein the at least one signature was previously determined using the at least one trained machine learning model; and when it is determined that the second set includes the first set, outputting an indication that the first asset was correctly associated with the first set of assets.

Abstract: Techniques for associating assets related to events detected in at least one computer network with respective assets in an asset catalog for the at least one computer network. The techniques comprising: obtaining information about an event related to a first asset, the information specifying computer network addressing information for the first asset; generating a signature of the first asset from the computer network addressing information using at least one trained machine learning model, wherein the signature comprises a numeric representation of the first asset; associating the first asset with at least one asset in the asset catalog using the signature and at least one signature of the at least one asset in the asset catalog, wherein the at least one signature was previously determined using the at least one trained machine learning model; and outputting information identifying the at least one asset with which the first asset was associated.

Abstract: Various embodiments include systems and methods of implementing a machine learning model for calculating confidence scores associated with potential security vulnerabilities. The machine learning model is trained using vulnerability data associated with a set of previously identified vulnerabilities, where the vulnerability data indicates whether a previously identified vulnerability is a true positive or a false positive. In some embodiments, scan traffic data may be obtained. The scan traffic data may be associated with potential security vulnerabilities detected via scan engine(s) that implement application security testing. The machine learning model may be used to determine respective confidence scores for each potential security vulnerability. According to some embodiments, responsive to a request for scan findings associated with a particular application, the respective confidence scores may be displayed via a vulnerability analysis graphical user interface.

Abstract: Some embodiments provide a vulnerability data processing system that uses machine learning (ML) to identify anomalous vulnerability data among vulnerability data acquired for configuring vulnerability detection of a computer network security system configured to monitor a computing environment. The system obtains vulnerability data that comprises values of a vulnerability parameter. The system generates datapoints representing values of the vulnerability parameter included in the obtained vulnerability data. The system clusters the datapoints to obtain vulnerability parameter clusters. The system identifies anomalous vulnerability data using the vulnerability parameter clusters.

Abstract: Techniques for associating assets related to events detected in at least one computer network with respective assets in an asset catalog for the at least one computer network. The techniques comprising: obtaining information about an event related to a first asset, the information specifying computer network addressing information for the first asset; generating a signature of the first asset from the computer network addressing information using at least one trained machine learning model, wherein the signature comprises a numeric representation of the first asset; associating the first asset with at least one asset in the asset catalog using the signature and at least one signature of the at least one asset in the asset catalog, wherein the at least one signature was previously determined using the at least one trained machine learning model; and outputting information identifying the at least one asset with which the first asset was associated.

Abstract: Various embodiments include systems and methods of implementing a machine learning model for calculating confidence scores associated with potential security vulnerabilities. The machine learning model is trained using vulnerability data associated with a set of previously identified vulnerabilities, where the vulnerability data indicates whether a previously identified vulnerability is a true positive or a false positive. In some embodiments, scan traffic data may be obtained. The scan traffic data may be associated with potential security vulnerabilities detected via scan engine(s) that implement application security testing. The machine learning model may be used to determine respective confidence scores for each potential security vulnerability. According to some embodiments, responsive to a request for scan findings associated with a particular application, the respective confidence scores may be displayed via a vulnerability analysis graphical user interface.

Abstract: Machine learning techniques for updating a configuration of a computer network security system operating in a cloud computing environment. The techniques include obtaining a plurality of datasets containing information about a respective plurality of events detected by the computer network security system in the cloud computing environment; generating, using at least one trained ML model, a plurality of signatures representing the plurality of events, the generating comprising processing the plurality of datasets using the at least one trained ML model to obtain the plurality of signatures; clustering the plurality of signatures to obtain signature clusters representing clusters of events in the plurality of events; identifying a particular event cluster from among the clusters of events; and updating the configuration of the computer network security system based on characteristics of events in the identified particular event cluster.

Abstract: Machine learning techniques for updating a configuration of a computer network security system operating in a cloud computing environment. The techniques include obtaining a plurality of datasets containing information about a respective plurality of events detected by the computer network security system in the cloud computing environment; generating, using at least one trained ML model, a plurality of signatures representing the plurality of events, the generating comprising processing the plurality of datasets using the at least one trained ML model to obtain the plurality of signatures; clustering the plurality of signatures to obtain signature clusters representing clusters of events in the plurality of events; identifying a particular event cluster from among the clusters of events; and updating the configuration of the computer network security system based on characteristics of events in the identified particular event cluster.

Abstract: Techniques for associating assets related to events detected in at least one computer network with respective assets in an asset catalog for the at least one computer network. The techniques comprising: obtaining information about an event related to a first asset, the information specifying computer network addressing information for the first asset; generating a signature of the first asset from the computer network addressing information using at least one trained machine learning model, wherein the signature comprises a numeric representation of the first asset; associating the first asset with at least one asset in the asset catalog using the signature and at least one signature of the at least one asset in the asset catalog, wherein the at least one signature was previously determined using the at least one trained machine learning model; and outputting information identifying the at least one asset with which the first asset was associated.

Abstract: Techniques for associating assets related to events detected in at least one computer network with respective assets in an asset catalog for the at least one computer network. The techniques comprising: obtaining information about an event related to a first asset, the information specifying computer network addressing information for the first asset; generating a signature of the first asset from the computer network addressing information using at least one trained machine learning model, wherein the signature comprises a numeric representation of the first asset; associating the first asset with at least one asset in the asset catalog using the signature and at least one signature of the at least one asset in the asset catalog, wherein the at least one signature was previously determined using the at least one trained machine learning model; and outputting information identifying the at least one asset with which the first asset was associated.

Abstract: Techniques for verifying correctness of associations between assets related to events detected in at least one computer network and assets in an asset catalog for the at least one computer network. The techniques include obtaining information specifying a first asset and a first set of assets with which the first asset was previously associated; generating a signature of the first asset from the computer network addressing information for the first asset; generating a hashed signature by applying a locality sensitive hashing (LSH) technique to the signature; associating the first asset with a second set of assets in the asset catalog using the hashed signature and at least one hashed signature of the at least one asset in the asset catalog; and when it is determined that the second set of includes the first set, outputting an indication that the first asset was correctly associated with the first set of assets.

Abstract: Techniques for associating assets related to events detected in at least one computer network with respective assets in an asset catalog for the at least one computer network. The techniques include: while monitoring activity on the at least one computer network, obtaining information about an event related to a first asset, the information specifying computer network addressing information for the first asset; generating a signature of the first asset from the computer network addressing information; generating a hashed signature of the first asset by applying a locality sensitive hashing (LSH) technique to the signature; associating the first asset with at least one asset in the asset catalog using the hashed signature of the first asset and at least one hashed signature of the at least one asset in the asset catalog; and outputting information identifying the at least one asset with which the first asset was associated.

Abstract: Techniques for associating assets related to events detected in at least one computer network with respective assets in an asset catalog for the at least one computer network. The techniques comprising: obtaining information about an event related to a first asset, the information specifying computer network addressing information for the first asset; generating a signature of the first asset from the computer network addressing information using at least one trained machine learning model, wherein the signature comprises a numeric representation of the first asset; associating the first asset with at least one asset in the asset catalog using the signature and at least one signature of the at least one asset in the asset catalog, wherein the at least one signature was previously determined using the at least one trained machine learning model; and outputting information identifying the at least one asset with which the first asset was associated.

Abstract: Techniques for verifying correctness of associations between assets related to events detected in at least one computer network and assets in an asset catalog for the at least one computer network. The techniques include: obtaining information specifying a first asset and a first set of assets with which the first asset was previously associated; generating a signature of the first asset from computer network addressing information for the first asset using at least one trained machine learning model; associating the first asset with a second set of assets using the signature and at least one signature of the at least one asset, wherein the at least one signature was previously determined using the at least one trained machine learning model; and when it is determined that the second set includes the first set, outputting an indication that the first asset was correctly associated with the first set of assets.