公开(公告)号：US11481495B2

公开(公告)日：2022-10-25

申请号：US16410675

申请日：2019-05-13

申请人： SRI International ,  The Trustees of Princeton University

发明人： Sek M. Chai ,  Zecheng He ,  Aswin Nadamuni Raghavan ,  Ruby B. Lee

IPC分类号： G06F21/56 ,  G06N7/00 ,  H04L9/40 ,  G06F11/34

摘要： A method, apparatus and system for anomaly detection in a processor based system includes training a deep learning sequence prediction model using observed baseline behavioral sequences of at least one processor behavior of the processor based system, predicting baseline behavioral sequences from the observed baseline behavioral sequences using the sequence prediction model, determining a baseline reconstruction error distribution profile using the baseline behavioral sequences and the predicted baseline behavioral sequences, predicting test behavioral sequences from observed, test behavioral sequences using the sequence prediction model, determining a testing reconstruction error distribution profile using the observed test behavioral sequences and the predicted test behavioral sequences, and comparing the baseline reconstruction error distribution profile to the testing reconstruction error distribution profile to determine if an anomaly exists in a processor behavior of the processor based system.

公开(公告)号：US11328206B2

公开(公告)日：2022-05-10

申请号：US15625578

申请日：2017-06-16

申请人： SRI International

发明人： Sek M. Chai ,  David C. Zhang ,  Mohamed R. Amer ,  Timothy J. Shields ,  Aswin Nadamuni Raghavan ,  Bhaskar Ramamurthy

IPC分类号： G06N3/04 ,  G06N3/063 ,  G06N3/08 ,  G06F9/46 ,  G06F9/50

摘要： Operations of computing devices are managed using one or more deep neural networks (DNNs), which may receive, as DNN inputs, data from sensors, instructions executed by processors, and/or outputs of other DNNs. One or more DNNs, which may be generative, can be applied to the DNN inputs to generate DNN outputs based on relationships between DNN inputs. The DNNs may include DNN parameters learned using one or more computing workloads. The DNN outputs may be, for example, control signals for managing operations of computing devices, predictions for use in generating control signals, warnings indicating an acceptable state is predicted, and/or inputs to one or more neural networks. The signals enhance performance, efficiency, and/or security of one or more of the computing devices. DNNs can be dynamically trained to personalize operations by updating DNN weights or other parameters.

公开(公告)号：US20170364792A1

公开(公告)日：2017-12-21

申请号：US15625578

申请日：2017-06-16

申请人： SRI International

发明人： Sek M. Chai ,  David C. Zhang ,  Mohamed R. Amer ,  Timothy J. Shields ,  Aswin Nadamuni Raghavan ,  Bhaskar Ramamurthy

IPC分类号： G06N3/04 ,  G06N3/08 ,  G06N3/063

CPC分类号： G06N3/0454 ,  G06F9/46 ,  G06F9/50 ,  G06N3/0445 ,  G06N3/063 ,  G06N3/08

摘要： Operations of computing devices are managed using one or more deep neural networks (DNNs), which may receive, as DNN inputs, data from sensors, instructions executed by processors, and/or outputs of other DNNs. One or more DNNs, which may be generative, can be applied to the DNN inputs to generate DNN outputs based on relationships between DNN inputs. The DNNs may include DNN parameters learned using one or more computing workloads. The DNN outputs may be, for example, control signals for managing operations of computing devices, predictions for use in generating control signals, warnings indicating an acceptable state is predicted, and/or inputs to one or more neural networks. The signals enhance performance, efficiency, and/or security of one or more of the computing devices. DNNs can be dynamically trained to personalize operations by updating DNN weights or other parameters.

公开(公告)号：US20200293657A1

公开(公告)日：2020-09-17

申请号：US16410675

申请日：2019-05-13

申请人： SRI International

发明人： Sek M. Chai ,  Zecheng He ,  Aswin Nadamuni Raghavan ,  Ruby B. Lee

IPC分类号： G06F21/56 ,  G06N7/00 ,  G06F11/34 ,  H04L29/06

摘要： A method, apparatus and system for anomaly detection in a processor based system includes training a deep learning sequence prediction model using observed baseline behavioral sequences of at least one processor behavior of the processor based system, predicting baseline behavioral sequences from the observed baseline behavioral sequences using the sequence prediction model, determining a baseline reconstruction error distribution profile using the baseline behavioral sequences and the predicted baseline behavioral sequences, predicting test behavioral sequences from observed, test behavioral sequences using the sequence prediction model, determining a testing reconstruction error distribution profile using the observed test behavioral sequences and the predicted test behavioral sequences, and comparing the baseline reconstruction error distribution profile to the testing reconstruction error distribution profile to determine if an anomaly exists in a processor behavior of the processor based system.