Abstract: One or more execution traces of an application are accessed. The one or more execution traces have been collected at a basic block level. Basic blocks in the one or more execution traces are scored. Scores for the basic blocks represent benefits of performing binary slimming at the corresponding basic blocks. Runtime binary slimming is performed of the application based on the scores of the basic blocks.

Abstract: A neural network is trained using a training data set, resulting in a set of model weights, namely, a matrix X, corresponding to the trained network. The set of model weights is then modified to produce a locked matrix X?, which is generated by applying a key. In one embodiment, the key is a binary matrix {0, 1} that zeros (masks) out certain neurons in the network, thereby protecting the network. In another embodiment, the key comprises a matrix of sign values {?1, +1}. In yet another embodiment, the key comprises a set of real values. Preferably, the key is derived by applying a key derivation function to a secret value. The key is symmetric, such that the key used to protect the model weight matrix X (to generate the locked matrix) is also used to recover that matrix, and thus enable access to the model as it was trained.

Abstract: Anomalous control and data flow paths in a program are determined by machine learning the program's normal control flow paths and data flow paths. A subset of those paths also may be determined to involve sensitive data and/or computation. Learning involves collecting events as the program executes, and associating those event with metadata related to the flows. This information is used to train the system about normal paths versus anomalous paths, and sensitive paths versus non-sensitive paths. Training leads to development of a baseline “provenance” graph, which is evaluated to determine “sensitive” control or data flows in the “normal” operation. This process is enhanced by analyzing log data collected during runtime execution of the program against a policy to assign confidence values to the control and data flows. Using these confidence values, anomalous edges and/or paths with respect to the policy are identified to generate a “program execution” provenance graph associated with the policy.

Abstract: Anomalous control and data flow paths in a program are determined by machine learning the program's normal control flow paths and data flow paths. A subset of those paths also may be determined to involve sensitive data and/or computation. Learning involves collecting events as the program executes, and associating those event with metadata related to the flows. This information is used to train the system about normal paths versus anomalous paths, and sensitive paths versus non-sensitive paths. Training leads to development of a baseline “provenance” graph, which is evaluated to determine “sensitive” control or data flows in the “normal” operation. This process is enhanced by analyzing log data collected during runtime execution of the program against a policy to assign confidence values to the control and data flows. Using these confidence values, anomalous edges and/or paths with respect to the policy are identified to generate a “program execution” provenance graph associated with the policy.

Abstract: Handshake protocol layer features are extracted from training data associated with encrypted network traffic of a plurality of classified devices. Record protocol layer features are extracted from the training data. One or more models are trained based on the extracted handshake protocol layer features and the extracted record protocol layer features. The one or more models are applied to an observed encrypted network traffic stream associated with a device to determine a predicted device classification of the device.

Abstract: Managing passwords is provided. A machine training process is performed using a set of existing passwords to train a machine learning component. Members of a set of semantic categories are used to categorize respective passwords in the set of existing passwords. Password strengths corresponding to a set of candidate passwords are evaluated using the machine learning component. A resource is secured with a candidate password having a password strength greater than or equal to a defined password strength threshold level.

Abstract: Managing passwords is provided. A machine training process is performed using a set of existing passwords to train a machine learning component. Members of a set of semantic categories are used to categorize respective passwords in the set of existing passwords. Password strengths corresponding to a set of candidate passwords are evaluated using the machine learning component. A resource is secured with a candidate password having a password strength greater than or equal to a defined password strength threshold level.

Abstract: Using a deep learning inference system, respective similarities are measured for each of a set of intermediate representations to input information used as an input to the deep learning inference system. The deep learning inference system includes multiple layers, each layer producing one or more associated intermediate representations. Selection is made of a subset of the set of intermediate representations that are most similar to the input information. Using the selected subset of intermediate representations, a partitioning point is determined in the multiple layers used to partition the multiple layers into two partitions defined so that information leakage for the two partitions will meet a privacy parameter when a first of the two partitions is prevented from leaking information. The partitioning point is output for use in partitioning the multiple layers of the deep learning inference system into the two partitions.

Abstract: One embodiment provides a method comprising receiving a training set comprising a plurality of data points, where a neural network is trained as a classifier based on the training set. The method further comprises, for each data point of the training set, classifying the data point with one of a plurality of classification labels using the trained neural network, and recording neuronal activations of a portion of the trained neural network in response to the data point. The method further comprises, for each classification label that a portion of the training set has been classified with, clustering a portion of all recorded neuronal activations that are in response to the portion of the training set, and detecting one or more poisonous data points in the portion of the training set based on the clustering.

Abstract: Unused instructions and no longer used instructions in a target application binary are determined. The target application binary is rewritten before and after runtime execution of the target application binary to remove the unused and no longer used instructions to reduce binary attack surface area for the runtime execution of the target application binary. Methods, computer systems, and computer program products are disclosed.

Abstract: Unused instructions and no longer used instructions in a target application binary are determined. The target application binary is rewritten before and after runtime execution of the target application binary to remove the unused and no longer used instructions to reduce binary attack surface area for the runtime execution of the target application binary. Methods, computer systems, and computer program products are disclosed.

Abstract: Unused instructions and no longer used instructions in a target application binary are determined. The target application binary is rewritten before and after runtime execution of the target application binary to remove the unused and no longer used instructions to reduce binary attack surface area for the runtime execution of the target application binary. Methods, computer systems, and computer program products are disclosed.

Abstract: Anomalous control and data flow paths in a program are determined by machine learning the program's normal control flow paths and data flow paths. A subset of those paths also may be determined to involve sensitive data and/or computation. Learning involves collecting events as the program executes, and associating those event with metadata related to the flows. This information is used to train the system about normal paths versus anomalous paths, and sensitive paths versus non-sensitive paths. Training leads to development of a baseline “provenance” graph, which is evaluated to determine “sensitive” control or data flows in the “normal” operation. This process is enhanced by analyzing log data collected during runtime execution of the program against a policy to assign confidence values to the control and data flows. Using these confidence values, anomalous edges and/or paths with respect to the policy are identified to generate a “program execution” provenance graph associated with the policy.

Abstract: A method for anomaly detection on a system or application used by a plurality of users includes providing an access to a memory device storing user data samples of a usage of the system or application for all users of the plurality of users. A target user is selected from among the plurality of users, using a processor on a computer, with data samples of the target user forming a cluster of data points in a data space. The data samples for the target user are used to generate a normal sample data set as training data set for training a model for an anomaly detection monitor for the target user. A local outlier factor (LOF) function is used to generate an abnormal sample data set for training the anomaly detection monitor for the target user.

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: Managing passwords is provided. A machine training process is performed using a set of existing passwords to train a machine learning component. Members of a set of semantic categories are used to categorize respective passwords in the set of existing passwords. Password strengths corresponding to a set of candidate passwords are evaluated using the machine learning component. A resource is secured with a candidate password having a password strength greater than or equal to a defined password strength threshold level.

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 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: Applications of machine learning techniques such as Latent Dirichlet Allocation (LDA) and author-topic models (ATM) to the problems of mining of user roles to specify access control policies from entitlement as well as logs which contain record of the usage of these entitlements are provided. In one aspect, a method for performing role mining given a plurality of users and a plurality of permissions is provided. The method includes the following steps. At least one generative machine learning technique, e.g., LDA, is used to obtain a probability distribution ? for user-to-role assignments and a probability distribution ? for role-to-permission assignments. The probability distribution ? for user-to-role assignments and the probability distribution ? for role-to-permission assignments are used to produce a final set of roles, including user-to-role assignments and role-to-permission assignments.

Abstract: Techniques for creating training sets for predictive modeling are provided. In one aspect, a method for generating training data from an unlabeled data set is provided which includes the following steps. A small initial set of data is selected from the unlabeled data set. Labels are acquired for the initial set of data selected from the unlabeled data set resulting in labeled data. The data in the unlabeled data set is clustered using a semi-supervised clustering process along with the labeled data to produce data clusters. Data samples are chosen from each of the clusters to use as the training data. The selecting, presenting, clustering and choosing steps are repeated with one or more additional sets of data selected from the unlabeled data set until a desired amount of training data has been obtained, wherein at each iteration an amount of the labeled data is increased.