Abstract: A framework is provided for modeling the activity surrounding user credentials and/or machine level activity on a computer network using computer event logs by viewing the logs attributed to each user as a multivariate data stream. The methodology performs well in detecting compromised user credentials at a very low false positive rate. Such a methodology may detect both users of compromised credentials by external actors and otherwise authorized users who have begun engaging in malicious activity.

Abstract: Businesses typically have large amounts of data about customer transactions and other customer information which is not fully utilized. The present invention provides a means of using this information to make predictions about future customer behavior, for example by estimating the probability that a customer will leave a bank. Using these predictions the business is able to take action in order to improve its performance. Using customer data a Bayesian statistical model is generated and this model used to generate statistical estimators of customer behavior. The statistical model is formed using hidden Markov model techniques by clustering customer data and attributes (e.g. Age, sex, salary) into a finite number of states. The number of states is unobserved and considered random. Bayesian prior probability distributions are specified and combined with the data to produce Bayesian posterior probability distributions.

Abstract: Businesses typically have large amounts of data about customer transactions and other customer information which is not fully utilized. The present invention provides a means of using this information to make predictions about future customer behavior, for example by estimating the probability that a customer will leave a bank. Using these predictions the business is able to take action in order to improve its performance. Using customer data a Bayesian statistical model is generated and this model used to generate statistical estimators of customer behavior. The statistical model is formed using hidden Markov model techniques by clustering customer data and attributes (e.g. Age, sex, salary) into a finite number of states. The number of states is unobserved and considered random. Bayesian prior probability distributions are specified and combined with the data to produce Bayesian posterior probability distributions.