A system includes a memory, a survey engine, and a reporting engine. The memory stores identifying information of a plurality of users. The survey engine determines a question to present to each user of the plurality of users and determines an interval for each user of the plurality of users. The determined interval for a first user of the plurality of users is different from the determined interval for a second user of the plurality of users. For each user, the survey engine communicates to that user, based on the stored identifying information, the determined question for that user according to the determined interval for that user and receives a response from each user of the plurality of users. The reporting engine generates a report based on the received response from the plurality of users.

A method for detecting a defect may include extracting, from application code and using a framework support specification corresponding to a framework, a framework interaction between the application code and the framework. The framework interaction specifies an object used by the application code and managed by the framework. The method may further include performing, using the framework interaction, a dynamic analysis of the application code to obtain a heap snapshot, performing, using the heap snapshot and the framework interaction, a static analysis of the application code, and detecting, by the static analysis, the defect.

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.

Computer environment infrastructure compliance audit result prediction includes receiving system inventory information identifying systems of a computer environment and properties of those systems, loading security requirements applicable to systems, determining compliance deviations indicating deviations between current configurations of the systems and the security requirements, based at least on the determined compliance deviations, selecting audit features based on which a predicted audit result is to be generated, and generating a predicted audit result using the selected audit features as input to an audit result classification model trained on historical audit information to predict audit results based on input audit features, and the predicted audit result being a prediction of a result of an audit of the systems.

An example method includes determining, based on a static scan, that a software container image or an intended execution environment of the software container image meets one or more first criteria required to exploit a software vulnerability. Based on the determining, runtime behavior of a software container instantiated from the software container image is monitored. The monitoring including determining whether the software container meets one or more second criteria required to exploit the software vulnerability, wherein the one or more first second criteria differs from the one or more second criteria. Based on the runtime monitoring, a risk score that indicates a magnitude of a risk the software vulnerability poses for the software container is determined, and a notification of the risk score is provided. A system for assessing software containers for vulnerabilities is also disclosed.

There is provided a cryptographic key determination device for determining one or more cryptographic keys in a cryptographic device, the cryptographic device being configured to execute one or more test programs, the cryptographic device comprising one or more components (11-i), each component (11-i) being configured to generate static and dynamic data, the dynamic data being generated in response to the execution of the one or more test programs, wherein the cryptographic key determination device comprises: a data extraction unit configured to extract at least one part of the static data and at least one part of the dynamic data generated by the one or more components (11-i), and a key generator configured to combine the at least one part of static data and the at least one part of dynamic data, and to determine the one or more cryptographic keys by applying a cryptographic function to the combined data.

Disclosed herein are systems and methods for enabling the automatic detection of executable code from a stream of bytes. In some embodiments, the stream of bytes can be sourced from the hidden areas of files that traditional malware detection solutions ignore. In some embodiments, a machine learning model is trained to detect whether a particular stream of bytes is executable code. Other embodiments described herein disclose systems and methods for automatic feature extraction using a neural network. Given a new file, the systems and methods may preprocess the code to be inputted into a trained neural network. The neural network may be used as a “feature generator” for a malware detection model. Other embodiments herein are directed to systems and methods for identifying, flagging, and/or detecting threat actors which attempt to obtain access to library functions independently.

A system, product and process for building a package dependencies data structure. The method comprises determining a package instance identifier of the package to be processed. The package instance identifier is determined based on a unique identifier of the package to be processed and based on a unique identifier of a dependency sub-tree of the package to be processed in the package instance identifier. The package instance identifier is utilized to determine if a node representing the package to be processed exists or not. In case the node does not exist already in the data structure, the node is created and added.

A computer-implemented method includes: receiving system information data representing configurations of digital systems; receiving attack information data associated one or more of the digital systems; analyzing the received system information data and attack information data, to associated attack types; identifying, for each identified attack type, correlations and/or causalities between individual system constituents or combinations thereof in the digital systems associated with attacks; determining and assigning, based on the identified correlations and/or causalities, an attack vulnerability value, for each attack, respectively, to each of the systems and/or systems' constituents and/or combinations thereof; and retrievably storing attack vulnerability values associated with the systems, system constituents and/or combinations thereof.

An attack information processing apparatus (10) includes an extraction unit (11) configured to extract first and second attack knowledge pieces indicating conditions of a cyber attack from first and second attack information pieces including descriptions of the cyber attack, a determination unit (12) configured to determine similarity between the first and second attack information pieces, and a complementing unit (13) configured to complement the first attack knowledge piece with the second attack knowledge piece based on the determined similarity.