Abstract: Sanitizing a virtual machine image of sensitive data is provided. Labeling dependencies and sanitization dependencies between a plurality of software components in the virtual machine image are identified based on labeling execution policies located in a labeler module and sanitization execution policies located in a sanitizer module, respectively. The labeler module and the sanitizer module are inserted in the virtual machine image. A sensitivity level label of a plurality of sensitivity labels is attached to identified sensitive data from the sensitive data contained in the virtual machine image based on the identified labeling dependencies. In response to receiving an input to perform a sanitization of the identified sensitive data having attached sensitivity level labels contained in the virtual machine image, the sanitization of the identified sensitive data having the attached sensitivity level labels contained in the virtual machine image is performed based on the identified sanitization dependencies.

Abstract: Generating communities of users and discovering the expertise of those users are provided. Identifications of a plurality of users that accessed resources via a network, types of actions performed by the plurality of users on the resources, and names of the resources accessed by the plurality of users are extracted from retrieved resource access logs. The plurality of users are grouped into a plurality of different sets of users based on which resources were accessed and which type of actions were performed by each particular user within the plurality of users. The communities of users are generated based on each different set of users having a similarity with regard to the resources that were accessed and the types of actions that were performed by a particular set of users.

Abstract: Embodiments include a network data collection and response system for enhancing security in an enterprise network providing a user-supplied computing device with access to the network. A network data collection and response system tracks network activity of the device and maintains a device inventory recording the device type and configuration information for the device along with a resource utilization profile for the device. The network data collection and response system detects high-risk or unauthorized network activity involving the device through passive monitoring without utilization of a data monitoring agent installed on the device and implements a response action to mitigate the high-risk or unauthorized network.

Abstract: Portions of code in an original application are randomized to generate a randomized version of the original application, wherein the randomizing does not modify expected behavior of the original application. Digital signature(s) are generated that attest to integrity of the randomized version. The digital signature(s) and either the original application or the randomized version are sent to a user device for execution or denial of execution of the randomized version based on the digital signature(s). At the user device, the randomized version is created if not received. The randomized version of the application is verified by the user device using the digital signature(s). The randomized version is executed by the user device in response to the digital signature(s) being verified or not executing the randomized version in response to the digital signature(s) not being verified.

Abstract: Automatically generating audit logs is provided. Audit log statement insertion points are identified in components of an application based on a static code analysis identifying start and end operations on sensitive data in the components of the application. The application is instrumented with audit log statements at the audit log statement insertion points in the components of the application. Audit logs of monitored sensitive data activity events in the application are generated using the audit log statements at the audit log statement insertion points in the components of the application.

Abstract: Generating an attack graph is provided. A set of sensitive data corresponding to a regulated service is identified. A set of components corresponding to the regulated service that are authorized to perform activities associated with sensitive data is scanned for. Vulnerability and risk metrics corresponding to each component in the set of components of the regulated service is identified. The attack graph that includes nodes representing components in the set of components of the regulated service and edges between nodes representing relationships between related components in the set of components is generated based on the vulnerability and risk metrics corresponding to each component in the set of components.

Abstract: Detecting malicious user activity is provided. A profile for a user that accesses a set of protected assets is generated based on static information representing an organizational view and associated attributes corresponding to the user and based on dynamic information representing observable actions made by the user. A plurality of analytics is applied on the profile corresponding to the user to generate an aggregate risk score for the user accessing the set of protected assets based on applying the plurality of analytics on the profile of the user. A malicious user activity alert is generated in response to the aggregate risk score for the user accessing the set of protected assets being greater than an alert threshold value. The malicious user activity alert is sent to an analyst for feedback.

Abstract: Techniques for inferring security policies from semantic attributes are provided. In one aspect, a method for building a query component executable by a processor is provided. The method includes the steps of: (a) providing subjects and permissions related to making a security policy decision, as well as a training set of permission-to-subject assignments, as inputs to the security policy query component; (b) extracting semantic attributes from natural language freeform text descriptions of the subjects and the permissions; and (c) using machine learning to build the security policy query component based on the permission-to-subject assignments in the training set and the semantic attributes extracted in step (b).

Abstract: Log(s) of IT events are accessed in a distributed system that includes a distributed application. The distributed system includes multiple data objects. The distributed application uses, processes, or otherwise accesses one or more of data objects. The IT events concern the distributed application and concern accesses by the distributed application to the data object(s). The IT events are correlated with a selected set of the data objects. Risks are estimated to the selected set of data objects based on the information technology events. Estimating risks uses at least ranks of compliance rules as these rules apply to the data objects in the system, and vulnerability scores of systems corresponding to the set of data objects and information technology events. Information is output that allows a user to determine the estimated risks for the selected set of data objects. Techniques for determining ranks of compliance rules are also disclosed.

Abstract: A technique is provided for continuous user authentication through real-time fusion and correlation of multiple factors. Monitored data is continuously obtained from a computer. The monitored data is related to user actions on the computer of a user. A server analyzes the monitored data of the computer to execute a windowing system event sequences modality, a network footprint modality, an application specific user actions modality, and/or a forensic linguistic analysis modality for the user. The user is authenticated on the computer based on a combination of the windowing system event sequences modality, the network footprint modality, the application specific user actions modality, and/or the forensic linguistic analysis modality.

Abstract: Identifying fraudulent transactions is provided. A transaction payment relationship graph that represents relationships of a plurality of financial transactions between accounts is generated utilizing transaction log data from one or more different transaction channels. A probability is calculated that an edge exists from any account vertex to another account vertex in the transaction payment relationship graph based on features extracted from the transaction payment relationship graph. The calculated probability that the edge exists between account vertices corresponding to the current financial transaction is a vertex link prediction. A fraud score for a current financial transaction is calculated based on the calculated probability that the edge exists between account vertices corresponding to the current transaction.

Abstract: A method (and structure) generates a classifier for an anomalous detection monitor for a target user on a system or application used by a plurality of users and includes providing an access to a memory device storing user data samples for all users of the plurality of users. A target user is selected from among the plurality of users. Data samples for the target user and data samples for other users of the plurality of users are used to generate a normal sample data set and an abnormal (anomalous) sample data set to serve as a training data set for training a model for an anomaly detection monitor for the target user.

Abstract: A method for detecting abnormal behavior of users is disclosed. Processors identify from a log of user activity, a first number of actions performed by a user over a first time period that match a pattern of user activity for a task associated with one or more roles of the users. Processors also identify from the log of user activity, a second number of actions performed by the user over a second time period that match the pattern of user activity. Processors calculate an amount of deviation between the first number of actions and the second number of actions. The deviation identifies a difference between amounts of time spent in the one or more roles. Processors then determine whether the amount of deviation between the first number of actions and the second number of actions exceeds a threshold for abnormal behavior.

Abstract: Identifying fraudulent transactions is provided. Transactions data corresponding to a plurality of transactions between accounts are obtained from one or more different transaction channels. At least one graph of transaction payment relationships between the accounts is generated from the transaction data. Features are extracted from the at least one graph of transaction payment relationships between the accounts. A fraud score for a current transaction is generated based on the extracted features from the at least one graph of transaction payment relationships between the accounts.

Abstract: Techniques for inferring security policies from semantic attributes are provided. In one aspect, a method for building a query component executable by a processor is provided. The method includes the steps of: (a) providing subjects and permissions related to making a security policy decision, as well as a training set of permission-to-subject assignments, as inputs to the security policy query component; (b) extracting semantic attributes from natural language freeform text descriptions of the subjects and the permissions; and (c) using machine learning to build the security policy query component based on the permission-to-subject assignments in the training set and the semantic attributes extracted in step (b).

Abstract: Sanitizing a virtual machine image of sensitive data is provided. Labeling dependencies and sanitization dependencies between a plurality of software components in the virtual machine image are identified based on labeling execution policies located in a labeler module and sanitization execution policies located in a sanitizer module, respectively. The labeler module and the sanitizer module are inserted in the virtual machine image. A sensitivity level label of a plurality of sensitivity labels is attached to identified sensitive data from the sensitive data contained in the virtual machine image based on the identified labeling dependencies. In response to receiving an input to perform a sanitization of the identified sensitive data having attached sensitivity level labels contained in the virtual machine image, the sanitization of the identified sensitive data having the attached sensitivity level labels contained in the virtual machine image is performed based on the identified sanitization dependencies.

Abstract: Sanitizing a virtual machine image of sensitive data is provided. Labeling dependencies and sanitization dependencies between a plurality of software components in the virtual machine image are identified based on labeling execution policies located in a labeler module and sanitization execution policies located in a sanitizer module, respectively. The labeler module and the sanitizer module are inserted in the virtual machine image. A sensitivity level label of a plurality of sensitivity labels is attached to identified sensitive data from the sensitive data contained in the virtual machine image based on the identified labeling dependencies. In response to receiving an input to perform a sanitization of the identified sensitive data having attached sensitivity level labels contained in the virtual machine image, the sanitization of the identified sensitive data having the attached sensitivity level labels contained in the virtual machine image is performed based on the identified sanitization dependencies.

Abstract: Sanitizing a virtual machine image of sensitive data is provided. A label for a sensitivity level is attached to identified sensitive data contained within each software component in a plurality of software components of a software stack in a virtual machine image based on labeling policies. In response to receiving an input to perform a sanitization of the identified sensitive data having attached sensitivity level labels contained within software components of the software stack in the virtual machine image, the sanitization of the identified sensitive data having the attached sensitivity level labels contained within the software components of the software stack in the virtual machine image is performed based on sanitization policies.

Abstract: Sanitizing a virtual machine image of sensitive data is provided. A label for a sensitivity level is attached to identified sensitive data contained within each software component in a plurality of software components of a software stack in a virtual machine image based on labeling policies. In response to receiving an input to perform a sanitization of the identified sensitive data having attached sensitivity level labels contained within software components of the software stack in the virtual machine image, the sanitization of the identified sensitive data having the attached sensitivity level labels contained within the software components of the software stack in the virtual machine image is performed based on sanitization policies.

Abstract: In one aspect, a method for managing a security policy having multiple policy items includes the steps of: (a) mapping permissions to the policy items which apply to usage of the permissions so as to determine which of the permissions are granted to groups of users by each of the policy items; (b) identifying at least one of the policy items mapped in step (a) that is in violation of least privilege based on a comparison of an actual permission usage with the security policy; (c) identifying at least one of the policy items mapped in step (a) that increases operational risk; (d) verifying that policy constructs in the security policy are consistent with policy constructs inferred from the actual permission usage; and (e) identifying optimizations of the security policy based on output from one or more of steps (a)-(d).