Abstract: An example system includes a processor to receive a data set for training a machine learning model. The processor can train the machine learning model on the data set. The processor can also translate the machine learning model into constraint satisfaction problem (CSP) variables and constraints. The processor can generate fabricated data based on the CSP variables and constraints.

Abstract: A computer-implemented method comprising, automatically: analyzing a machine learning dataset which comprises multiple datapoints, to deduce constraints on features of the datapoints; generating a first set of CSP (Constraint Satisfaction Problem) rules expressing the constraints; based on a machine learning model which was trained on the dataset, generating a second set of CSP rules that define one or more perturbation candidates among the features of one of the datapoints; formulating a CSP based on the first and second sets of CSP rules; solving the formulated CSP using a solver; and using the solution of the CSP as a counterfactual explanation of a prediction made by the machine learning model with respect to the one datapoint.

Abstract: Embodiments of the present systems and methods may provide encrypted biometric information that can be stored and used for authentication with undegraded recognition performance. For example, in an embodiment, a method may comprise storing a plurality of encrypted trained weights of a neural network classifier, wherein the weights have been trained using biometric information representing at least one biometric feature of a person, receiving encrypted biometric information obtained by sampling at least one biometric feature of the person and encrypting the sampled biometric feature, obtaining an match-score using the encrypted trained neural network classifier, the match-score indicating a probability that the received encrypted biometric information matches the stored encrypted biometric information, and authenticating the person when the probability that received encrypted biometric information matches the stored encrypted biometric information exceeds a threshold.

Abstract: Embodiments of the present systems and methods may provide encrypted biometric information that can be stored and used for authentication with undegraded recognition performance. For example, in an embodiment, a method may comprise storing a plurality of encrypted trained weights of a neural network classifier, wherein the weights have been trained using biometric information representing at least one biometric feature of a person, receiving encrypted biometric information obtained by sampling at least one biometric feature of the person and encrypting the sampled biometric feature, obtaining an match-score using the encrypted trained neural network classifier, the match-score indicating a probability that the received encrypted biometric information matches the stored encrypted biometric information, and authenticating the person when the probability that received encrypted biometric information matches the stored encrypted biometric information exceeds a threshold.

Abstract: Mitigating return-oriented programming attacks. From program code and associated components needed by the program code for execution, machine language instruction sequences that may be combined and executed as malicious code are selected. A predetermined number of additional copies of each of the selected machine language instruction sequences are made, and the additional copies are marked as non-executable. The machine language instruction sequences and the non-executable copies are distributed in memory. If a process attempts to execute a machine language instruction sequence that has been marked non-executable, the computer may initiate protective action.

Abstract: Mitigating return-oriented programming (ROP) attacks. Program code and associated components are received and loaded into memory. From the program code and associated components, a predetermined number of sequences of machine language instructions that terminate in a return instruction are selected. The sequences of machine language instructions include: machine language instruction sequences that are equivalent to a conditional statement “if-then-else return,” sequences of machine language instructions corresponding to known malicious code sequences, and sequences of machine language instructions corresponding to machine language instructions in known toolkits for assembling malicious code sequences.

Abstract: Mitigating return-oriented programming attacks. Program code and associated components are received and loaded into memory. From the program code and associated components, a predetermined number of sequences of machine language instructions that terminate in a return instruction are selected. The sequences of machine language instructions include: machine language instruction sequences that are equivalent to a conditional statement “if-then-else return,” sequences of machine language instructions corresponding to known malicious code sequences, and sequences of machine language instructions corresponding to machine language instructions in known toolkits for assembling malicious code sequences.

Abstract: Mitigating return-oriented programming attacks. From program code and associated components needed by the program code for execution, machine language instruction sequences that may be combined and executed as malicious code are selected. A predetermined number of additional copies of each of the selected machine language instruction sequences are made, and the additional copies are marked as non-executable. The machine language instruction sequences and the non-executable copies are distributed in memory. If a process attempts to execute a machine language instruction sequence that has been marked non-executable, the computer may initiate protective action.

Abstract: Mitigating return-oriented programming attacks. Program code and associated components are received and loaded into memory. From the program code and associated components, a predetermined number of sequences of machine language instructions that terminate in a return instruction are selected. The sequences of machine language instructions include: machine language instruction sequences that are equivalent to a conditional statement “if-then-else return,” sequences of machine language instructions corresponding to known malicious code sequences, and sequences of machine language instructions corresponding to machine language instructions in known toolkits for assembling malicious code sequences.

Abstract: Mitigating return-oriented programming (ROP) attacks. Program code and associated components are received and loaded into memory. From the program code and associated components, a predetermined number of sequences of machine language instructions that terminate in a return instruction are selected. The sequences of machine language instructions include: machine language instruction sequences that are equivalent to a conditional statement “if-then-else return,” sequences of machine language instructions corresponding to known malicious code sequences, and sequences of machine language instructions corresponding to machine language instructions in known toolkits for assembling malicious code sequences.

Abstract: Mitigating return-oriented programming attacks. From program code and associated components needed by the program code for execution, machine language instruction sequences that may be combined and executed as malicious code are selected. A predetermined number of additional copies of each of the selected machine language instruction sequences are made, and the additional copies are marked as non-executable. The machine language instruction sequences and the non-executable copies are distributed in memory. If a process attempts to execute a machine language instruction sequence that has been marked non-executable, the computer may initiate protective action.

Abstract: Methods and systems are disclosed that enhance the ability of a test generator to automatically deal with address translation in a processor design, and without need for creating specific code. A model of the address translation mechanism of a design-under-test is represented as a directed acyclic graph and then converted into a constraint satisfaction problem. The problem is solved by a CSP engine, and the solution used to generate test cases for execution. Using the model, testing knowledge can be propagated to models applicable to many different designs to produce extensive coverage of address translation mechanisms.

Abstract: Methods and systems are disclosed that enhance the ability of a test generator to automatically deal with address translation in a processor design, and without need for creating specific code. A model of the address translation mechanism of a design-under-test is represented as a directed acyclic graph and then converted into a constraint satisfaction problem. The problem is solved by a CSP engine, and the solution used to generate test cases for execution. Using the model, testing knowledge can be propagated to models applicable to many different designs to produce extensive coverage of address translation mechanisms.