Abstract: In general, various aspects of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for integrating third party computing system functionality into a first party computing system by providing a risk management and mitigation computing system configured to analyze a risk of integrating the functionality provided by the third party computing system and facilitating implementation of one or more data-related controls that include performing computer-specific operations to mitigate and/or eliminate the identified risks. For example, the risk management and mitigation computing system can access risk data in tenant computing systems to determine a risk score related to the integration of the third party computing system functionality based on risks determined during prior integrations of the third party computing system functionality by other tenant computing systems.

Abstract: Systems and techniques that facilitate universal and efficient privacy-preserving vertical federated learning are provided. In various embodiments, a key distribution component can distribute respective feature-dimension public keys and respective sample-dimension public keys to respective participants in a vertical federated learning framework governed by a coordinator, wherein the respective participants can send to the coordinator respective local model updates encrypted by the respective feature-dimension public keys and respective local datasets encrypted by the respective sample-dimension public keys. In various embodiments, an inference prevention component can verify a participant-related weight vector generated by the coordinator, based on which the key distribution component can distribute to the coordinator a functional feature-dimension secret key that can aggregate the encrypted respective local model updates into a sample-related weight vector.

Abstract: An electronic device is disclosed. The electronic device includes a memory, a camera module, a communications module, and a processor that is configured to: receive, from the camera module, image data associated with a machine-readable optical label, the optical label encoding transaction details of a transfer of data to a recipient account, wherein the transaction details do not indicate an identity of the recipient account; receive a user input indicating authorization to initiate a transfer of data, via a protected resource, from an account associated with the user to the recipient account; and in response to receiving the user input, generate a request for initiating the transfer of data based on the transaction details, the request including an access token for use in authenticating the user on requests to access the protected resource.

Abstract: Methods and systems for assessing internet exposure of a cloud-based workload are disclosed. A method comprises accessing at least one cloud provider API to determine a plurality of entities capable of routing traffic in a virtual cloud environment associated with a target account containing the workload, querying the at least one cloud provider API to determine at least one networking configuration of the entities, building a graph connecting the plurality of entities based on the networking configuration, accessing a data structure identifying services publicly accessible via the Internet and capable of serving as an internet proxy; integrating the identified services into the graph; traversing the graph to identify at least one source originating via the Internet and reaching the workload, and outputting a risk notification associated with the workload. Systems and computer-readable media implementing the above method are also disclosed.

Abstract: The present disclosure discloses a system and method for dynamically modifying role based access control for a client based on the activity. Generally, a client device is granted access to a network resource based on a first reputation score assigned to the client device. The activity of the client device is monitored. Responsive to monitoring the activity of the client device, a second reputation score is determined for the client device based on the activity. The access by the client device to the network resource is then modified to be granted based on the second reputation score.

Abstract: A method for characterizing network traffic is provided. The method includes maintaining a database identifying a plurality of digital certificates and a number of Internet Protocol addresses associated with each of the plurality of digital certificates, capturing network traffic over a network connection at a network connected device, analyzing the network traffic by determining the digital certificates associated with Internet Protocol addresses associated with the network traffic and a number of Internet Protocol addresses associated with each of the digital certificates and updating the database, and characterizing at least one of the Internet Protocol addresses associated with one of the digital certificates based on the number of Internet Protocol addresses associated with the one of the digital certificates.

Abstract: A non-transitory computer readable medium comprising instructions stored thereon, the instructions effective to cause at least one processor to: establish trustworthiness of an application installed on a endpoint, the established trustworthiness is sufficient for an enterprise security infrastructure to treat the application installed on the endpoint and the endpoint as a trusted application and a trusted endpoint; negotiate with the trusted endpoint to determine a traffic inspection method for traffic flows originating at the trusted application that is destined for a service, the traffic inspection method is determined based on at least the trusted application, and the service; and instruct the trusted application of the determined traffic inspection method.

Abstract: Systems and methods for predicting enterprise cyber incidents using social network analysis on the darkweb hacker forums are disclosed.

Abstract: A method of detecting the onset of a ransomware attack is presented. In an example embodiment, file backup metadata for each of a plurality of computing devices is accessed and analyzed to detect anomalous file backup activity of individual ones of the computing devices. A determination is made as to whether the detected anomalous file backup activity of at least some of the computing devices is correlated in time. File description metadata for each of the computing devices is also accessed and analyzed to identify files in the computing devices that are anomalous to other files in the computing devices. A determination whether a ransomware attack has begun is based on a determination that the detected anomalous file backup activity of at least some of the computing devices is correlated in time, as well as on the identified anomalous files.

Abstract: Systems, methods, and computer-readable media for application placement can include the following processes. A security score service determines a respective security posture score for each of a plurality of candidate hosts of an enterprise network. A user then identify a set of performance parameters and security parameters for a host in an enterprise network to execute a workload thereon. An application placement engine selects a host from the plurality of candidate hosts having a security posture score matching the performance parameters and the security parameters for executing the workload. An application deployment engine places the workload on the host.

Abstract: Aspects of the disclosure relate to identifying legitimate websites and removing false positives from domain discovery analysis. Based on a list of known legitimate domains, a computing platform may generate a baseline dataset of feature vectors corresponding to the known legitimate domains. Subsequently, the computing platform may receive information identifying a first domain for analysis and may execute one or more machine learning algorithms to compare the first domain to the baseline dataset. Based on execution of the one or more machine learning algorithms, the computing platform may generate first domain classification information indicating that the first domain is a legitimate domain. In response to determining that the first domain is a legitimate domain, the computing platform may send one or more commands directing a domain identification system to remove the first domain from a list of indeterminate domains maintained by the domain identification system.

Abstract: Various embodiments of a computer-implemented framework for predicting exploitation of software vulnerabilities are disclosed.

Abstract: Embodiments of the disclosure provide a system and method for developing rich data for holistic metrics for gauging an enterprise cyber security posture to enable proactive and preventative measures in order to minimize the enterprise's exposure to a cyberattack. By taking an enterprise-wide holistic approach to cyber security, the enterprise will have information needed to identify areas of its network systems for remediation that will result in making the enterprise a less attractive target for cyber threat actors.

Abstract: A mechanism is described herein for automatically detecting vulnerability remediations and regressions. A system may receive data indicating that a security alert exists for a specific vulnerability. The system retrieves parameters from the alert and generates (or retrieves) a script or a set of scripts for detecting the vulnerability based on those parameters. The script is executed to determine whether the vulnerability has been remediated or has regressed post remediation. If the system determined that the vulnerability has been remediated, it transmits a request to resolve the security alert. The script is then continually or periodically executed. If the system, through executing the script, determines that the vulnerability has been reintroduced into the environment (e.g., via a code upgrade or a parameter update), it reopens the existing alert indicating that the vulnerability has been reintroduced into the environment.

Abstract: In an embodiment, an asset may utilize one or more scanning techniques to detect a first set of software components that is not being natively tracked by an operating system of the asset, the one or more scanning techniques comprising one or more of an evaluation of metadata associated with one or more running processes of the asset, and an evaluation of file system information that characterizes the first set of software components. The asset may further store an indication of the first set of software components detected in accordance with the one or more scanning techniques, and may optionally report the indication to an external entity (e.g., a vulnerability management system).

Abstract: A method of cyber-risk assessment includes populating a database with ransomware attack information non-intrusively gathered from a plurality of data sources. A request for a ransomware susceptibility assessment of an entity associated with a domain name is received. A digital footprint of an entity is discovered in response to the associated domain name using non-intrusive information gathering. Commercial information associated with the entity is collected in response to the domain name. The database is scanned in response to the discovered digital footprint and at least one ransomware factor associated with the entity is generated in response to the scan. An impact parameter and a ransomware factor coefficient is computed based on the collected commercial information. Then an entity susceptibility index is computed based on the impact parameter, the ransomware factor coefficient, and the at least one ransomware factor.

Abstract: In some instances, the disclosure provides a method for identifying access anomalies using network graphs. The method comprises obtaining access data for an entity, generating a network graph baseline profile based on the plurality of data elements, generating a network graph current profile based on the plurality of data elements, generating comparison data based on comparing the plurality of baseline network graphs with the one or more current network graphs and comparing the plurality of baseline nodes and the plurality of baseline edges with the plurality of current nodes and the plurality of current edges, determining, based on the comparison data, anomaly data comprising one or more flagged network accesses to the enterprise system, and providing the anomaly data indicating the flagged network accesses to an authentication system.

Abstract: Systems and methods are described for using secured groups for simulated phishing campaigns to obfuscate data for levels of privacy based on protected criteria classes. Initially, a group to resolve members of the group based on multiple users matching one or more group criteria is established. It is then determined that at least one criteria of the one or more criteria has been configured as one of multiple protected criteria classes. Responsive to the determination, the group is identified as a secured group. A query of the group is then executed to identify one or more users of the multiple users as members of the group based on the users matching the criteria of the secured group at the time of execution of the group and information of the one or more users resulting from the execution of the secured group is obfuscated in accordance with the protected criteria class.

Abstract: Similar assets across a digital attack surface are identified. Extracting detail information and related edge information enables a network analysis system to provide indexed assets. A user of a network analysis system may provide additional data sources to enhance indexed assets. New data sources are processed in bulk to update existing assets. Edge information is pre-computed to provide on-demand access to a global inventory of mapped domain infrastructure assets.

Abstract: A system for inspecting data, the system comprising: at least one processor configured to: establish a trusted relationship between a source account in a cloud environment and a scanner account; using the established trust relationship, utilize at least one cloud provider API to identify workloads in the source account; use the at least one cloud provider API to query a geographical location of at least one of the identified workloads; receive an identification of the geographic location; use the cloud provider APIs to access block storage volumes of the at least one workload; determine a file-system of the at least one workload; mount the block storage volumes on a scanner based on the determined file-system; activate a scanner at the geographic location; reconstruct from the block storage volumes a state of the workload; and assess the reconstructed state of the workload to extract insights.