Abstract: Embodiments described herein provide a supervisor for fault management at a production system. During operation, the supervisor can obtain a set of sensor readings and a state of the production system. A respective sensor reading is an output of a sensor in the production system. The supervisor can then determine, using an artificial intelligence (AI) model, whether the set of sensor readings accommodates a fault associated with a corresponding sensor. Subsequently, the supervisor can determine an action that mitigates an effect of the fault and modify the set of sensor readings based on the action. Here, the modified set of sensor readings is used by a controller that controls the production system.

Abstract: One embodiment provides a system which facilitates construction of an ensemble of neural network-based classifiers that optimize a diversity metric. During operation, the system defines a diversity metric based on pairwise angles between decision boundaries of three or more affine classifiers. The system includes the diversity metric as a regularization term in a loss function optimization for designing a pair of mutually orthogonal affine classifiers of the three or more affine classifiers. The system trains one or more neural networks such that parameters of the one or more neural networks are consistent with parameters of the affine classifiers to obtain an ensemble of neural network-based classifiers which optimize the diversity metric. The system predicts an outcome for a testing data object based on the obtained ensemble of neural-network based classifiers which optimize the diversity metric.

Abstract: One embodiment provides a method and system which facilitates optimizing a pair of affine classifiers based on a diversity metric. During operation, the system defines a diversity metric based on an angle between decision boundaries of a pair of affine classifiers. The system includes the diversity metric as a regularization term in a loss function optimization for designing the pair of affine classifiers, wherein the designed pair of affine classifiers are mutually orthogonal. The system predicts an outcome for a testing data object based on the designed pair of mutually orthogonal affine classifiers.

Abstract: Embodiments described herein provide a design architecture for co-designing a controller and a watermarking signal for a cyber-physical system. During operation, the architecture can determine, in conjunction with each other, respective values of a first set of parameters indicating operations of the controller and a second set of parameters representing the watermarking signal. Here, the watermarking signal is combinable with a control signal from the controller for monitoring an output signal of the cyber-physical system for detecting malicious data at different time instances. Subsequently, the architecture can determine a state manager for determining the states of the cyber-physical system from the monitored output signal based on the first and second sets of parameters. The architecture can also determine a detector capable of identifying presence of an attack from the states of the cyber-physical system at a plurality of time instances using the watermarking signal.

Abstract: Embodiments described herein provide a supervisor for fault management at a production system. During operation, the supervisor can obtain a set of sensor readings and a state of the production system. A respective sensor reading is an output of a sensor in the production system. The supervisor can then determine, using an artificial intelligence (AI) model, whether the set of sensor readings accommodates a fault associated with a corresponding sensor. Subsequently, the supervisor can determine an action that mitigates an effect of the fault and modify the set of sensor readings based on the action. Here, the modified set of sensor readings is used by a controller that controls the production system.

Abstract: Systems and methods for indexing blockchain data in a blockchain system. These systems and methods receive a set of transactions from one or more transaction blocks of a blockchain, wherein the transactions in the set have been validated by one or more peer systems of the blockchain. The systems and methods further generate an index to one or more fields of one or more transactions in the set of transactions of the transaction block generate an index representative of at least one field in the set of transactions of the transaction block and provide the generated index for validation by a peer system of the blockchain. After receiving verification from at least a threshold number of peer systems that the generated index has been validated by the peer system, the generated index is stored as an index block in the blockchain.

Abstract: Embodiments provide a system and method for extracting configuration-related information for reasoning about the security and functionality of a composed system. During operation, the system determines, by a computing device, information sources associated with hardware and software components of a system, wherein the information sources include at least specification sheets, standard operating procedures, user manuals, and vulnerability databases. The system selects a set of categories of vulnerabilities in a vulnerability database, and ingests the information sources to obtain data in a normalized format. The system extracts, from the ingested information sources, configuration information, vulnerability information, dependency information, and functionality requirements to create a model for the system.

Abstract: Systems and methods for indexing blockchain data in a blockchain system. These systems and methods receive a set of transactions from one or more transaction blocks of a blockchain, wherein the transactions in the set have been validated by one or more peer systems of the blockchain. The systems and methods further generate an index to one or more fields of one or more transactions in the set of transactions of the transaction block generate an index representative of at least one field in the set of transactions of the transaction block and provide the generated index for validation by a peer system of the blockchain. After receiving verification from at least a threshold number of peer systems that the generated index has been validated by the peer system, the generated index is stored as an index block in the blockchain.

Abstract: The following relates generally to defense mechanisms and security systems. Broadly, systems and methods are disclosed that detect an anomaly in an Embedded Mission Specific Device (EMSD). Disclosed approaches include a meta-material antenna configured to receive a radio frequency signal from the EMSD, and a central reader configured to receive a signal from the meta-material antenna. The central reader may be configured to: build a finite state machine model of the EMSD based on the signal received from the meta-material antenna; and detect if an anomaly exists in the EMSD based on the built finite state machine model.

Abstract: A system for collecting building environmental sensor data includes a first data collection device having a first transceiver that is configured to receive data from proximate building environmental sensors and a second transceiver that is configured to communicate with an external service. The device will, upon detecting that the first transceiver is within a receiving range of a first building environmental sensor, communicatively connect with the first building environmental sensor to receive a data stream that comprises sensor data captured by the first building environmental sensor. The device will continue to receive the data stream until the first transceiver either receives all of the sensor data or moves out of the receiving range. The device will repeat this process for additional building environmental sensors. The device will use its second transceiver to transfer the sensor data to the external service.

Abstract: A building environmental sensor includes a sensing element for collecting measurements of environmental parameters such as temperature, humidity, light, sound or the absence or presence of gas. The sensor will: (a) detect that a data collection device is within a communication range of the sensor; (b) generate a data stream that includes the data that the sensor collected; (c) transmit the data stream to the first data collection device; (d) determine that a communication link between the sensor and the first data collection device was lost before the first data stream was fully transmitted; (e) detect that a second data collection device is within the communication range of the sensor; (f) generate a second data stream that includes the remaining data; and (g) transmit the second data stream to the second data collection device.

Abstract: A system for collecting building environmental sensor data includes a first data collection device having a first transceiver that is configured to receive data from proximate building environmental sensors and a second transceiver that is configured to communicate with an external service. The device will, upon detecting that the first transceiver is within a receiving range of a first building environmental sensor, communicatively connect with the first building environmental sensor to receive a data stream that comprises sensor data captured by the first building environmental sensor. The device will continue to receive the data stream until the first transceiver either receives all of the sensor data or moves out of the receiving range. The device will repeat this process for additional building environmental sensors. The device will use its second transceiver to transfer the sensor data to the external service.

Abstract: A certified application is installed onto a content creation device and a mobile certified application is installed onto a mobile device, the applications establish first and second trust relationships with the cloud service. The certified application and mobile certified application establish the third trust relationship via a proximity network. The mobile certified application generates a first ephemeral key pair having a private part. The certified application generates a second ephemeral key pair having a private part. The mobile certified application requests a service from the content creation device involving the transfer of data between the content creation device and the cloud service. The data is protected by at least one of the first and second ephemeral key pairs in response to invocation of the service. The service results in the data being stored at the cloud service and/or rendered at the content creation device.

Abstract: A method and system are provided which facilitate construction of an ensemble of neural network kernel classifiers. The system divides a training set into partitions. The system trains, based on the training set, a first neural network encoder to output a first set of features, and trains, based on each respective partition of the training set, a second neural network encoder to output a second set of features. The system generates, for each respective partition, based on the first and second set of features, kernel models which output a third set of features. The system classifies, by a classification model, the training set based on the third set of features. The generated kernel models for each respective partition and the classification model comprise the ensemble of neural network kernel classifiers. The system predicts a result for a testing data object based on the ensemble of neural network kernel classifiers.

Abstract: One embodiment provides a system which facilitates construction of an ensemble of machine learning models. During operation, the system determines a training set of data objects, wherein each data object is associated with one of a plurality of classes. The system divides the training set of data objects into a number of partitions. The system generates a respective machine learning model for each respective partition using a universal kernel function, which processes the data objects divided into a respective partition to obtain the ensemble of machine learning models. The system trains the machine learning models based on the data objects of the training set. The system predicts an outcome for a testing data object based on the ensemble of machine learning models and an ensemble decision rule.

Abstract: Embodiments provide a system and method for reasoning about the optimality of a configuration parameter of a distributed system. During operation, the system obtains a multi-layer graph for a system with a plurality of components, wherein the multi-layer graph comprises a configuration subgraph, a vulnerability subgraph, and a dependency subgraph. The system determines, based on the multi-layer graph, constraint relationships associated with configuration parameters for the components, wherein the constraint relationships include security constraints and functionality constraints. The system computes an unsatisfiable core which comprises a set of mutually incompatible constraints.

Abstract: Embodiments described herein provide a system for improving a classifier by computing a statistic for the utility of sharing data with a second party. The system may encrypt a set of class labels based on a public key/private key pair to obtain a set of encrypted class labels. The system may send a public key and the set of encrypted class labels to a second computing device. The system may receive an encrypted value computed by the second computing device based on the public key. The system may decrypt the encrypted value based on a private key to obtain a decrypted value. The system may then send a pair of encrypted values computed based on the decrypted value to the second computing device. The system may subsequently receive an encrypted utility statistic from the second computing device, and decrypt the encrypted utility statistic to obtain a decrypted utility statistic.

Abstract: The system determines a version space associated with a set of data comprising a pool of unlabeled samples and a first plurality of labeled samples, wherein the version space includes a first set of classifiers corresponding to the first plurality of labeled samples. The system selects, from the pool of unlabeled samples, a second plurality of unlabeled samples comprising informative samples and non-informative samples. A respective informative sample corresponds to a first hyperplane which intersects the version space, and a respective non-informative sample corresponds to a second hyperplane which does not intersect the version space. The system acquires labels corresponding to the second plurality of unlabeled samples to obtain a third plurality of labeled samples. The system updates the first set of classifiers based on the third plurality of labeled samples, thereby improving accuracy of the first set of classifiers.

Abstract: One embodiment provides a system which facilitates reasoning about classifiers. During operation, the system determines a plurality of neural networks. The system derives, from a respective neural network, a linear model, wherein the linear model is constructed based on an output of a penultimate layer of the respective neural network. The system trains the linear model based on activations of the penultimate layer. The system maps parameters of the trained linear model into a version space.

Abstract: The system generates evidence of a recommended configuration for a distributed system based on a plurality of configuration parameters. The system displays, on a screen of a user device, the evidence, which includes a list of configuration parameters, including a name, a current value, and a recommended value for a respective configuration parameter. The recommended value is obtained based on a strategy for optimizing security, functionality, or both. The system further displays interactive elements which allow the user to: view a resolution of a pair of mutually incompatible constraints resulting in the recommended value for the respective configuration parameter, wherein the resolution includes a name of the configuration parameter removed from the list and a reason for the removal; and view information associated with each of the pair of mutually incompatible constraints, wherein the information includes a name, a goal, a security impact, and a description of the constraint.