Intrusion detection via semantic fuzzing and message provenance

    公开(公告)号:US11689544B2

    公开(公告)日:2023-06-27

    申请号:US16085199

    申请日:2017-03-15

    Abstract: Intrusion detection systems and methods monitor legal control messages in an operational control system to detect subtly malicious sequences of control messages with undesirable emergent effects on devices in the operational control system. A message provenance component may investigate system-level correlations between messages rather than detecting if individual messages are anomalous. A semantic fuzzing component may search, based on the operational effect of candidate message sequences, the space of legal messages for sequences that cause actual harm. Behavior oracles may be used to test message sequences to identify sequences that induce drift towards a failure state. The intrusion detection system is able to prevent harm and disruption arising from control messages that individually appear legitimate and benign but that, in combination with other messages, can cause undesirable outcomes.

    ARTIFICIAL INTELLIGENCE FOR GENERATING STRUCTURED DESCRIPTIONS OF SCENES

    公开(公告)号:US20190304156A1

    公开(公告)日:2019-10-03

    申请号:US16230987

    申请日:2018-12-21

    Abstract: This disclosure describes techniques that include generating, based on a description of a scene, a movie or animation that represents at least one possible version of a story corresponding to the description of the scene. This disclosure also describes techniques for training a machine learning model to generate predefined data structures from textual information, visual information, and/or other information about a story, an event, a scene, or a sequence of events or scenes within a story. This disclosure also describes techniques for using GANs to generate, from input, an animation of motion (e.g., an animation or a video clip). This disclosure also describes techniques for implementing an explainable artificial intelligence system that may provide end users with information (e.g., through a user interface) that enables an understanding of at least some of the decisions made by the AI system.

    INTRUSION DETECTION VIA SEMANTIC FUZZING AND MESSAGE PROVENANCE

    公开(公告)号:US20190089722A1

    公开(公告)日:2019-03-21

    申请号:US16085199

    申请日:2017-03-15

    Abstract: Intrusion detection systems and methods monitor legal control messages in an operational control system to detect subtly malicious sequences of control messages with undesirable emergent effects on devices in the operational control system. A message provenance component may investigate system-level correlations between messages rather than detecting if individual messages are anomalous. A semantic fuzzing component may search, based on the operational effect of candidate message sequences, the space of legal messages for sequences that cause actual harm. Behavior oracles may be used to test message sequences to identify sequences that induce drift towards a failure state. The intrusion detection system is able to prevent harm and disruption arising from control messages that individually appear legitimate and benign but that, in combination with other messages, can cause undesirable outcomes.

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