Autonomous detection of incongruous behaviors
OwnerPhilippe Baumardinventor
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Abstract
Behavioral characteristics of at least a first machine component are monitored. A model that represents machine-to-machine interactions between at least the first machine component and at least a further machine component is generated. Using the monitored behavioral characteristics and the generated model, an incongruity of a behavior of at least the first machine component and the machine-to-machine interactions is computed, where the incongruity is predicted based on determining a discordance between an expectation of the system and the behavior and the machine-to-machine interactions, and wherein the predicting is performed without using a previously built normative rule of behavior and machine-to-machine interactions.
Claims17
A method comprising:
- monitoring, by a system including a processor and sensors, behavioral characteristics of at least a first machine component;
- generating, by the system through unsupervised learning, an endogenous and unsupervised Bayesian model that represents interrelations between local events and machine-to-machine interactions between at least the first machine component and at least a further machine component;
- predicting, by the system using the monitored behavioral characteristics and the generated model, an incongruity of a behavior of at least the first machine component and the machine-to-machine interactions, wherein the incongruity is predicted based on determining a singularity of the local events and the machine-to-machine interactions, and a discordance between a calculated expectation of the system and an observed behavior and the machine-to-machine interactions, and wherein the predicting is performed without using a previously built normative rule of behavior and machine-to-machine interactions;
- and
- performing, by the system, an action with respect to at least the first machine component in response to the predicted incongruity.
A system comprising:
- a non-transitory storage medium storing instructions;
- and
- at least one processor, the instructions executable on the at least one processor to:
- learn behaviors of machine components by auto-generating a Bayesian network from data associated with the machine components, without prior teaching, and without human intervention, the Bayesian network providing, at each stage of a continual observation by the Bayesian network, a predictive model of the behaviors of the machine components and interactions of the machine components, without accessing functions of the machine components that direct these behaviors;
- compute, based on a predicting of a probable transformation of a behavior of a given machine component and based on the Bayesian network, a probabilistic prediction of an incongruity of the behavior of the given machine component;
- identify a second machine component that likely caused the incongruity;
- and
- perform an action to address the incongruity.
An article comprising at least one non-transitory machine-readable storage medium storing instructions that upon execution cause a system to:
- monitor behavioral characteristics of at least a first machine component;
- generate a Bayesian network that represents machine-to-machine interactions between at least the first machine component and at least a further machine component;
- predict, using the monitored behavioral characteristics and the generated model, an incongruity of a behavior of at least the first machine component and the machine-to-machine interactions, wherein the incongruity is predicted based on determining a discordance between an expectation of the system and the behavior and the machine-to-machine interactions, and wherein the predicting is performed without using a previously built normative rule of behavior and machine-to-machine interactions;
- and
- perform an action to address the predicted incongruity.
Dependencies are inferred from the claim text.