1326 results about "Bayesian network" patented technology

Selected system calls are monitored to generate frequency data that is input to a probabilistic intrusion detection analyzer which generates a likelihood score indicative of whether the system calls being monitored were produced by a computer system whose security has been compromised. A first Bayesian network is trained on data from a compromised system and a second Bayesian network is trained on data from a normal system. The probabilistic intrusion detection analyzer considers likelihood data from both Bayesian networks to generate the intrusion detection measure.

A method of generating graphical models for providing security analysis in computer networks that in one embodiment includes the steps of generating a type abstract graph independent of particular networks that models abstract dependency relationships among attributes and exploits; generating network-specific attack graphs by combining the type abstract graph with specific network information; monitoring an intruder alert; and generating a real-time attack graph by correlating the intruder alert with the network-specific attack graph. The real-time attack graph can be generated using reachability checking, bridging, and exploit prediction based on consequence alerts and may further include the step of calculating the likelihood of queries using a Bayesian network model. The method may also include the steps of inferring unobserved attacks that may have been missed by intrusion detection sensors, and projecting on which hosts and using what exploits additional intruder attacks may occur. The method may further include the step of comparing alternate actions by computation, wherein the alternate actions include the step of patching some vulnerabilities, and wherein the specific network information includes network topology. The specific network information may also include firewall rules.

A method for probabilistic record linkage includes providing a record pair comprising a plurality of fields, providing a plurality of scenarios, each scenario relating to a distribution of patterns among a plurality of attribute statuses, and comparing the record pair to determine a record difference. The method includes determining a probability of a status for each of a plurality of attributes based on the distance metric of the plurality of fields, wherein each field corresponds to a respective attribute, wherein the field is observable and the attribute is hidden, determining a probability of each scenario based on the probability of the status for each attribute and the Bayesian net representing the probabilistic model on the relationship between scenarios and attributes, and outputting a probability of duplication or non-duplication of the record pair determined from the probabilities of the plurality of scenarios.

The invention discloses an application management method. The method comprises the following steps: obtaining current application scene information, a pre-stored first application probability of each application program applied under each application scene, and a second application probability of each application program applied under each application scene during triggering cleaning of background application programs; inputting the current application scene information, all the first application probabilities and all the second application probabilities into a preset Bayesian network probability calculating model and calculating a third application probability of each background application program applied under the current application scene; and closing the background application programs with third application probabilities being smaller than a preset probability threshold value. The invention also discloses a mobile terminal and a computer-readable storage medium. Through the application management method, the accuracy of application management of the mobile terminal can be improved.

A system and method for determining image similarity. The method includes the steps of automatically providing perceptually significant features of main subject or background of a first image; automatically providing perceptually significant features of main subject or background of a second image; automatically comparing the perceptually significant features of the main subject or the background of the first image to the main subject or the background of the second image; and providing an output in response thereto. In the illustrative implementation, the features are provided by a number of belief levels, where the number of belief levels are preferably greater than two. The perceptually significant features include color, texture and/or shape. In the preferred embodiment, the main subject is indicated by a continuously valued belief map. The belief values of the main subject are determined by segmenting the image into regions of homogenous color and texture, computing at least one structure feature and at least one semantic feature for each region, and computing a belief value for all the pixels in the region using a Bayes net to combine the features. In an illustrative application, the inventive method is implemented in an image retrieval system. In this implementation, the inventive method automatically stores perceptually significant features of the main subject or background of a plurality of first images in a database to facilitate retrieval of a target image in response to an input or query image. Features corresponding to each of the plurality of stored images are automatically sequentially compared to similar features of the query image. Consequently, the present invention provides an automatic system and method for controlling the feature extraction, representation, and feature-based similarity retrieval strategies of a content-based image archival and retrieval system based on an analysis of main subject and background derived from a continuously valued main subject belief map.

To improve network reliability and management in today's high-speed communication networks, we propose an intelligent system using adaptive statistical approaches. The system learns the normal behavior of the network. Deviations from the norm are detected and the information is combined in the probabilistic framework of a Bayesian network. The proposed system is thereby able to detect unknown or unseen faults. As demonstrated on real network data, this method can detect abnormal behavior before a fault actually occurs, giving the network management system (human or automated) the ability to avoid a potentially serious problem.

A method and a computer implemented system for improving an active decision making process by using a simulation model of the decision making process. The simulation model is used to evaluate the impact of alternative decisions at a choice point, in order to select one alternative. The method or system may be integrated with an external system, like a manufacturing execution system. The simulation model may be stochastic, may be updated from monitoring the external system or the simulations, or may contain a Bayesian network

A method and system are disclosed for processing data from a computer network to determine an occurrence of and characterize a particular activity associated with the computer network. In accordance with exemplary embodiments of the present invention, a collection of data is managed that corresponds to events associated with the computer network. At least one model is established to correlate an occurrence of a predetermined set of events. At least one hypothesis is formed, using the at least one model, that characterizes the particular activity associated with the computer network. The at least one hypothesis is evaluated using the at least one model. The steps of forming and evaluating are performed interactively with the step of managing to iteratively update the collection of data.

Systems, methods, and devices are described for implementing map matching techniques relating to measured location data. Probabilistic models, including temporal Bayesian network models and Hidden Markov Models, may be used for combining multiple classes of evidence relating to potential locations of points traversed on routes over time. Multiple route segments and overall routes may be maintained under relative uncertainty as candidates. The candidate route segments and overall routes may then be reduced into a smaller number of candidates or a single most likely route as a trip progresses. As the trip progresses, route segments in proximity to each location point are identified and candidate matches are determined. A probability of an entity traversing a candidate match at a given time and a probability of an entity traversing between a first candidate match at a first time and a second candidate match at a second time are determined based on a plurality of factors. Different modalities may be used to measure and transmit the location data.

An integrated risk management tool includes a persistent object database to store information about actors (individuals and/or groups), physical surroundings, historical events and other information. The risk management tool also includes a decision support system that uses data objects from the database and advanced decision theory techniques, such as Bayesian Networks, to infer the relative risk of an undesirable event. As part of the relative risk calculation, the tool uses a simulation and gaming environment in which artificially intelligent actors interact with the environment to determine susceptibility to the undesired event. Preferred embodiments of the tool also include an open “plug-in” architecture that allows the tool to interface with existing consequence calculators. The tool also provides facilities for presenting data in a user-friendly manner as well as report generation facilities.

A data processing system generates recommendations for on-line shopping by scoring recommendations matching the customer's cart contents using by assessing and ranking each candidate recommendation by the expected incremental margin associated with the recommendation being issued (as compared to the expected margin associated with the recommendation not being issued) by taking into consideration historical associations, knowledge of the layout of the site, the complexity of the product being sold, the user's session behavior, the quality of the selling point messages, product life cycle, substitutability, demographics and/or other considerations relating to the customer purchase environment. In an illustrative implementation, scoring inputs for each candidate recommendation (such as relevance, exposure, clarity and/or pitch strength) are included in a probabilistic framework (such as a Bayesian network) to score the effectiveness of the candidate recommendation and/or associated selling point messages by comparing a recommendation outcome (e.g., purchase likelihood or expected margin resulting from a given recommendation) against a non-recommendation outcome (e.g., the purchase likelihood or expected margin if no recommendation is issued). In addition, a probabilistic framework may also be used to select a selling point message for inclusion with a selected candidate recommendation by assessing the relative strength of the selling point messages by factoring in a user profile match factor (e.g., the relative likelihood that the customer matches the various user case profiles).

A system for the representation, editing, evaluation, and inference of graphical models is disclosed which can be used to construct and evaluate a graphical model or graphical network and to calculate inference values. An efficient method of updating graphical models is demonstrated, and provides the basis for an improved system for manipulation and evaluation of probabilistic models. The graphical network editor is useful in the construction of graphical modes such as Bayesian Networks. The graphical network and network graphical user interface (GUI) are used in conjunction with each other to model a system wherein failure probabilities and the current state of components are taken into account to monitor the health and progress of a system for an engineer or engineering software to evaluate and monitor. The evaluation is useful in the monitoring of assets and other real systems having multiple, dependent, and independently operating components such as a pump, a manufacturing plant, a production line, an assembly line, where asset health and quality control is a concern. The asset components each influencing some overall outcome of a system or situation. Success or failure or probability of success, probability of failure and health of the system can be monitored and manipulated by altering the values of prior probability and posterior probability values. Failure correlation between components can be evaluated wherein failure rates of asset is unknown. Production and quality can be monitored and altered.

Described invention and its embodiments, in part, facilitate discovery of ‘Most Recent Common Ancestors’ in the family trees between a massive plurality of individuals who have been predicted to be related according to amount of deoxyribonucleic acids (DNA) shared as determined from a plurality of 3rd party genome sequencing and matching systems. This facilitation is enabled through a holistic set of distributed software Agents running, in part, a plurality of cooperating Machine Learning systems, such as smart evolutionary algorithms, custom classification algorithms, cluster analysis and geo-temporal proximity analysis, which in part, enable and rely on a system of Knowledge Management applied to manually input and data-mined evidences and hierarchical clusters, quality metrics, fuzzy logic constraints and Bayesian network inspired inference sharing spanning across and between all data available on personal family trees or system created virtual trees, and employing all available data regarding the genome-matching results of Users associated to those trees, and all available historical data influencing the subjects in the trees, which are represented in a form of Competitive Learning network. Derivative results of this system include, in part, automated clustering and association of phenotypes to genotypes, automated recreation of ancestor partial genomes from accumulated DNA from triangulations and the traits correlated to that DNA, and a system of cognitive computing based on distributed neural networks with mobile Agents mediating activation according to connection weights.

Fault trees are automatically converted to Bayesian networks for assisting in system reliability, failure analysis and diagnostics by using information from the fault tree structure to create the Bayesian network structure, creating parameters of the Bayesian network using information from the fault tree, obtaining information about observation nodes for the Bayesian network from a list of observations that augments information contained in the fault tree, and inserting the observation nodes into the Bayesian network. The fault tree is pre-processed into an intermediate format prior to conversion that may include adding reliability values from a separate text document when the fault tree is in such format that requires it.

A method and system managing radio frequency identification (RFID) systems. The system monitors the performance of systems that use RFID tags and readers. It operates at multiple levels to ensure the optimal performance of readers, tags, antennae, and the information processing systems that acquire and convey tag data. The management system may employ artificial intelligence techniques such as genetic algorithms, fuzzy logic, neural networks, Bayesian networks, support vector machines or statistical methods to develop, maintain and exploit models of RFID system behavior. By comparing the actual performance of RFIDs and related components, the management system can detect and report failures and partial failures of components. The management system may also send signals to components to enhance performance of the overall RFID system.

The invention discloses a human face emotion recognition method in a mobile embedded complex environment. In the method, a human face is divided into main areas of a forehead, eyebrows and eyes, cheeks, a noise, a month and a chain, and 68 feature points are further divided. In view of the above feature points, in order to realize the human face emotion recognition rate, the accuracy and the reliability in various environments, a human face and expression feature classification method is used in a normal condition, a Faster R-CNN face area convolution neural network-based method is used in conditions of light, reflection and shadow, a method of combining a Bayes Network, a Markoff chain and variational reasoning is used in complex conditions of motion, jitter, shaking, and movement, and a method of combining a deep convolution neural network, a super-resolution generative adversarial network (SRGANs), reinforcement learning, a backpropagation algorithm and a dropout algorithm is used in conditions of incomplete human face display, a multi-human face environment and noisy background. Thus, the human face expression recognition effects, the accuracy and the reliability can be promoted effectively.

Illness signatures are mathematically characterized by entrainment relationships among multiple time series representations of physiological processes. Such characteristics include time and phase lags, window lengths for optimum detection, which time series are most entrained with each other, the degree of entrainment relative to the rest of the large database, and the concordance or discordance of the time-varying changes. These optimum disease-specific characteristics can be determined, for example, from large, clinically well-annotated databases of time series representations of physiological processes during health and illness. These characteristics of the entrainment relationships among multiple time series representations of physiological processes are used to make mathematical and statistical predictive models using multivariable techniques such as, but not limited to, logistic regression, nearest-neighbor techniques, neural and Bayesian networks, principal and other component analysis, and others. These models are quantitative expressions that transform measured characteristics to the probability of an illness, or p(illness).

The invention discloses a method for performing fault diagnosis by using model conversion. According to the method, processed related information of a fault mode effect analysis model is converted into a corresponding Bayesian network model by using a self-defined data structure while ensuring complete data, an elementary event, a logic gate and an intermediate event of a fault tree in a fault tree analysis model are converted into nodes in a Bayesian network respectively, and a corresponding conditional probability table in the Bayesian network is set. The fault diagnosis is performed through the converted Bayesian network model. The method of the invention expands the use of the Bayesian network model in the fault diagnosis, improves the diagnosis accuracy of a fault diagnosis model in practical application, ensures the universality of model conversion, and can realize cross-tool conversion among different fault mode effect analysis, fault tree analysis result and the generated Bayesian network.