Abstract: A method for determining a probability of a hidden variable from an observed variable in a Dynamic Bayesian Network is presented. The method includes identifying the network based on predetermined criteria, determining a number of hidden variables in a time slice of the network, determining a number of the time slices of the network, and determining the probability of the hidden variable from the observed variable in less than exponential time with respect to the number of hidden variables.

Abstract: A method, apparatus, and computer program product are presented for automatically evaluating Bayesian network models. Operations performed comprise receiving a Bayesian Network (BN) model including evidence nodes and conclusion nodes that are linked with the evidence nodes by causal dependency links, and where the evidence nodes have evidence states and the conclusion nodes have conclusion states. The states of conclusion nodes are set to desired conclusion states and corresponding probabilities of occurrence of evidence states are determined by propagating these states down the causal dependency links. Thus, samples of most likely states of the evidence nodes are generated. Then, states of the evidence nodes are set corresponding to the samples of the evidence states. These states are propagated back up the causal dependency links to obtain probabilities of the resulting states of the conclusion nodes. Finally, a representation is outputted for the probabilities of the states of the conclusion nodes.

Abstract: Methods, apparatus, and systems are provided for distributed hypothesis testing in autonomic processing machines. Evidence about a network resource is gathered or contributed to and associated with a hypothesis about the network resource. The evidence is processed to determine whether a decision can be made with respect to activities or problems associated with the network resource. The hypothesis, the evidence, and the contributed evidence are propagated over a network to other network nodes or participants.

Abstract: A method for determining a probability of a hidden variable from an observed variable in a Dynamic Bayesian Network is presented. The method includes identifying the network based on predetermined criteria, determining a number of hidden variables in a time slice of the network, determining a number of the time slices of the network, and determining the probability of the hidden variable from the observed variable in less than exponential time with respect to the number of hidden variables.

Abstract: Methods, apparatus, and systems are provided for distributed hypothesis testing in autonomic processing machines. Evidence about a network resource is gathered or contributed to and associated with a hypothesis about the network resource. The evidence is processed to determine whether a decision can be made with respect to activities or problems associated with the network resource. The hypothesis, the evidence, and the contributed evidence are propagated over a network to other network nodes or participants.

Abstract: A method and apparatus for collaborative attack detection in networks. An embodiment of a method comprises generating a first security belief for a first element of a network, receiving a second security belief for a second element of a network, and revising the first security belief based at least in part on the second security belief.

Abstract: A method, apparatus, and computer program product are presented for automatically evaluating Bayesian network models. Operations performed comprise receiving a Bayesian Network (BN) model including evidence nodes and conclusion nodes that are linked with the evidence nodes by causal dependency links, and where the evidence nodes have evidence states and the conclusion nodes have conclusion states. The states of conclusion nodes are set to desired conclusion states and corresponding probabilities of occurrence of evidence states are determined by propagating these states down the causal dependency links. Thus, samples of most likely states of the evidence nodes are generated. Then, states of the evidence nodes are set corresponding to the samples of the evidence states. These states are propagated back up the causal dependency links to obtain probabilities of the resulting states of the conclusion nodes. Finally, a representation is outputted for the probabilities of the states of the conclusion nodes.