Patents by Inventor Guang-Tong Zhou
Guang-Tong Zhou has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
-
Patent number: 11579951Abstract: Techniques are described herein for predicting disk drive failure using a machine learning model. The framework involves receiving disk drive sensor attributes as training data, preprocessing the training data to select a set of enhanced feature sequences, and using the enhanced feature sequences to train a machine learning model to predict disk drive failures from disk drive sensor monitoring data. Prior to the training phase, the RNN LSTM model is tuned using a set of predefined hyper-parameters. The preprocessing, which is performed during the training and evaluation phase as well as later during the prediction phase, involves using predefined values for a set of parameters to generate the set of enhanced sequences from raw sensor reading. The enhanced feature sequences are generated to maintain a desired healthy/failed disk ratio, and only use samples leading up to a last-valid-time sample in order to honor a pre-specified heads-up-period alert requirement.Type: GrantFiled: September 27, 2018Date of Patent: February 14, 2023Assignee: Oracle International CorporationInventors: Onur Kocberber, Felix Schmidt, Arun Raghavan, Nipun Agarwal, Sam Idicula, Guang-Tong Zhou, Nitin Kunal
-
Patent number: 11451565Abstract: Techniques are provided herein for contextual embedding of features of operational logs or network traffic for anomaly detection based on sequence prediction. In an embodiment, a computer has a predictive recurrent neural network (RNN) that detects an anomalous network flow. In an embodiment, an RNN contextually transcodes sparse feature vectors that represent log messages into dense feature vectors that may be predictive or used to generate predictive vectors. In an embodiment, graph embedding improves feature embedding of log traces. In an embodiment, a computer detects and feature-encodes independent traces from related log messages. These techniques may detect malicious activity by anomaly analysis of context-aware feature embeddings of network packet flows, log messages, and/or log traces.Type: GrantFiled: September 5, 2018Date of Patent: September 20, 2022Assignee: Oracle International CorporationInventors: Guang-Tong Zhou, Hossein Hajimirsadeghi, Andrew Brownsword, Stuart Wray, Craig Schelp, Rod Reddekopp, Felix Schmidt
-
Patent number: 11218498Abstract: Techniques are provided herein for contextual embedding of features of operational logs or network traffic for anomaly detection based on sequence prediction. In an embodiment, a computer has a predictive recurrent neural network (RNN) that detects an anomalous network flow. In an embodiment, an RNN contextually transcodes sparse feature vectors that represent log messages into dense feature vectors that may be predictive or used to generate predictive vectors. In an embodiment, graph embedding improves feature embedding of log traces. In an embodiment, a computer detects and feature-encodes independent traces from related log messages. These techniques may detect malicious activity by anomaly analysis of context-aware feature embeddings of network packet flows, log messages, and/or log traces.Type: GrantFiled: September 5, 2018Date of Patent: January 4, 2022Assignee: Oracle International CorporationInventors: Hossein Hajimirsadeghi, Guang-Tong Zhou, Andrew Brownsword, Nipun Agarwal, Pavan Chandrashekar, Karoon Rashedi Nia
-
Patent number: 11082438Abstract: Techniques are provided herein for contextual embedding of features of operational logs or network traffic for anomaly detection based on sequence prediction. In an embodiment, a computer has a predictive recurrent neural network (RNN) that detects an anomalous network flow. In an embodiment, an RNN contextually transcodes sparse feature vectors that represent log messages into dense feature vectors that may be predictive or used to generate predictive vectors. In an embodiment, graph embedding improves feature embedding of log traces. In an embodiment, a computer detects and feature-encodes independent traces from related log messages. These techniques may detect malicious activity by anomaly analysis of context-aware feature embeddings of network packet flows, log messages, and/or log traces.Type: GrantFiled: September 5, 2018Date of Patent: August 3, 2021Assignee: Oracle International CorporationInventors: Juan Fernandez Peinador, Manel Fernandez Gomez, Guang-Tong Zhou, Hossein Hajimirsadeghi, Andrew Brownsword, Onur Kocberber, Felix Schmidt, Craig Schelp
-
Publication number: 20200104200Abstract: Techniques are described herein for predicting disk drive failure using a machine learning model. The framework involves receiving disk drive sensor attributes as training data, preprocessing the training data to select a set of enhanced feature sequences, and using the enhanced feature sequences to train a machine learning model to predict disk drive failures from disk drive sensor monitoring data. Prior to the training phase, the RNN LSTM model is tuned using a set of predefined hyper-parameters. The preprocessing, which is performed during the training and evaluation phase as well as later during the prediction phase, involves using predefined values for a set of parameters to generate the set of enhanced sequences from raw sensor reading. The enhanced feature sequences are generated to maintain a desired healthy/failed disk ratio, and only use samples leading up to a last-valid-time sample in order to honor a pre-specified heads-up-period alert requirement.Type: ApplicationFiled: September 27, 2018Publication date: April 2, 2020Inventors: ONUR KOCBERBER, FELIX SCHMIDT, ARUN RAGHAVAN, NIPUN AGARWAL, SAM IDICULA, GUANG-TONG ZHOU, NITIN KUNAL
-
Publication number: 20200076842Abstract: Techniques are provided herein for contextual embedding of features of operational logs or network traffic for anomaly detection based on sequence prediction. In an embodiment, a computer has a predictive recurrent neural network (RNN) that detects an anomalous network flow. In an embodiment, an RNN contextually transcodes sparse feature vectors that represent log messages into dense feature vectors that may be predictive or used to generate predictive vectors. In an embodiment, graph embedding improves feature embedding of log traces. In an embodiment, a computer detects and feature-encodes independent traces from related log messages. These techniques may detect malicious activity by anomaly analysis of context-aware feature embeddings of network packet flows, log messages, and/or log traces.Type: ApplicationFiled: September 5, 2018Publication date: March 5, 2020Inventors: GUANG-TONG ZHOU, HOSSEIN HAJIMIRSADEGHI, ANDREW BROWNSWORD, STUART WRAY, CRAIG SCHELP, ROD REDDEKOPP, FELIX SCHMIDT
-
Publication number: 20200076840Abstract: Techniques are provided herein for contextual embedding of features of operational logs or network traffic for anomaly detection based on sequence prediction. In an embodiment, a computer has a predictive recurrent neural network (RNN) that detects an anomalous network flow. In an embodiment, an RNN contextually transcodes sparse feature vectors that represent log messages into dense feature vectors that may be predictive or used to generate predictive vectors. In an embodiment, graph embedding improves feature embedding of log traces. In an embodiment, a computer detects and feature-encodes independent traces from related log messages. These techniques may detect malicious activity by anomaly analysis of context-aware feature embeddings of network packet flows, log messages, and/or log traces.Type: ApplicationFiled: September 5, 2018Publication date: March 5, 2020Inventors: JUAN FERNANDEZ PEINADOR, MANEL FERNANDEZ GOMEZ, GUANG-TONG ZHOU, HOSSEIN HAJIMIRSADEGHI, ANDREW BROWNSWORD, ONUR KOCBERBER, FELIX SCHMIDT, CRAIG SCHELP
-
Publication number: 20200076841Abstract: Techniques are provided herein for contextual embedding of features of operational logs or network traffic for anomaly detection based on sequence prediction. In an embodiment, a computer has a predictive recurrent neural network (RNN) that detects an anomalous network flow. In an embodiment, an RNN contextually transcodes sparse feature vectors that represent log messages into dense feature vectors that may be predictive or used to generate predictive vectors. In an embodiment, graph embedding improves feature embedding of log traces. In an embodiment, a computer detects and feature-encodes independent traces from related log messages. These techniques may detect malicious activity by anomaly analysis of context-aware feature embeddings of network packet flows, log messages, and/or log traces.Type: ApplicationFiled: September 5, 2018Publication date: March 5, 2020Inventors: HOSSEIN HAJIMIRSADEGHI, GUANG-TONG ZHOU, ANDREW BROWNSWORD, NIPUN AGARWAL, PAVAN CHANDRASHEKAR, KAROON RASHEDI NIA
-
Patent number: 10540612Abstract: The disclosed embodiments relate to a system for validating a prognostic-surveillance mechanism, which detects anomalies that arise during operation of a computer system. During operation, the system obtains telemetry data comprising a set of raw signals gathered from sensors in the computer system during operation of the computer system, wherein the telemetry signals are gathered over a monitored time period. Next, for each raw signal in the set of raw signals, the system decomposes the raw signal into deterministic and stochastic components. The system then generates a corresponding set of synthesized signals based on the deterministic and stochastic components of the raw signals, wherein the synthesized signals are generated for a simulated time period, which is longer than the monitored time period. Finally, the system uses the set of synthesized signals to validate one or more performance metrics of the prognostic-surveillance mechanism.Type: GrantFiled: August 26, 2016Date of Patent: January 21, 2020Assignee: Oracle International CorporationInventors: Kenny C. Gross, Kalyanaraman Vaidyanathan, Guang-Tong Zhou
-
Publication number: 20180060151Abstract: The disclosed embodiments relate to a system for validating a prognostic-surveillance mechanism, which detects anomalies that arise during operation of a computer system. During operation, the system obtains telemetry data comprising a set of raw signals gathered from sensors in the computer system during operation of the computer system, wherein the telemetry signals are gathered over a monitored time period. Next, for each raw signal in the set of raw signals, the system decomposes the raw signal into deterministic and stochastic components. The system then generates a corresponding set of synthesized signals based on the deterministic and stochastic components of the raw signals, wherein the synthesized signals are generated for a simulated time period, which is longer than the monitored time period. Finally, the system uses the set of synthesized signals to validate one or more performance metrics of the prognostic-surveillance mechanism.Type: ApplicationFiled: August 26, 2016Publication date: March 1, 2018Applicant: Oracle International CorporationInventors: Kenny C. Gross, Kalyanaraman Vaidyanathan, Guang-Tong Zhou