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公开(公告)号：US11423327B2
公开(公告)日：2022-08-23
申请号：US16156925
申请日：2018-10-10
Applicant: Oracle International Corporation
Inventor： Onur Kocberber , Felix Schmidt , Craig Schelp , Andrew Brownsword , Nipun Agarwal
Abstract: Techniques are described herein for estimating CPU, memory, and I/O utilization for a workload via out-of-band sensor readings using a machine learning model. The framework involves receiving sensor data associated with executing benchmark applications, obtaining ground truth utilization values for the benchmarks, preprocessing the training data to select a set of enhanced sequences, and using the enhanced sequences to train a random forest model to estimate CPU, memory, and I/O utilization given sensor monitoring data. Prior to the training phase, a machine learning model is trained using a set of predefined hyper-parameters. The trained models are used to generate estimations for CPU, memory, and I/O utilizations values. The utilization values are used with workload context information to assess the deployment and generate one or more recommendations for machine types that will best serve the workload in terms of system utilization.
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公开(公告)号：US10768982B2
公开(公告)日：2020-09-08
申请号：US16135802
申请日：2018-09-19
Applicant: Oracle International Corporation
Inventor： Andrew Brownsword , Tayler Hetherington , Pavan Chandrashekar , Akhilesh Singhania , Stuart Wray , Pravin Shinde , Felix Schmidt , Craig Schelp , Onur Kocberber , Juan Fernandez Peinador , Rod Reddekopp , Manel Fernandez Gomez , Nipun Agarwal
Abstract: Herein are techniques for analysis of data streams. In an embodiment, a computer associates each software actor with data streams. Each software actor has its own backlog queue of data to analyze. In response to receiving some stream content and based on the received stream content, data is distributed to some software actors. In response to determining that the data satisfies completeness criteria of a particular software actor, an indication of the data is appended onto the backlog queue of the particular software actor. The particular software actor is reset to an initial state by loading an execution snapshot of a previous initial execution of an embedded virtual machine. Based on the particular software actor, execution of the execution snapshot of the previous initial execution is resumed to dequeue and process the indication of the data from the backlog queue of the particular software actor to generate a result.
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公开(公告)号：US11218498B2
公开(公告)日：2022-01-04
申请号：US16122505
申请日：2018-09-05
Applicant: Oracle International Corporation
Inventor： Hossein Hajimirsadeghi , Guang-Tong Zhou , Andrew Brownsword , Nipun Agarwal , Pavan Chandrashekar , Karoon Rashedi Nia
Abstract: 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.
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公开(公告)号：US11082438B2
公开(公告)日：2021-08-03
申请号：US16122398
申请日：2018-09-05
Applicant: Oracle International Corporation
Inventor： Juan Fernandez Peinador , Manel Fernandez Gomez , Guang-Tong Zhou , Hossein Hajimirsadeghi , Andrew Brownsword , Onur Kocberber , Felix Schmidt , Craig Schelp
Abstract: 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.
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公开(公告)号：US12020131B2
公开(公告)日：2024-06-25
申请号：US17221212
申请日：2021-04-02
Applicant: Oracle International Corporation
Inventor： Saeid Allahdadian , Amin Suzani , Milos Vasic , Matteo Casserini , Andrew Brownsword , Felix Schmidt , Nipun Agarwal
IPC: G06N20/20 , G06N3/04 , G06N3/0442 , G06N3/045 , G06N3/0495 , G06N3/08 , G06N3/088 , G06N20/00
CPC classification number: G06N20/20 , G06N3/04 , G06N3/0495 , G06N3/08 , G06N3/088 , G06N3/0442 , G06N3/045 , G06N20/00
Abstract: Techniques are provided for sparse ensembling of unsupervised machine learning models. In an embodiment, the proposed architecture is composed of multiple unsupervised machine learning models that each produce a score as output and a gating network that analyzes the inputs and outputs of the unsupervised machine learning models to select an optimal ensemble of unsupervised machine learning models. The gating network is trained to choose a minimal number of the multiple unsupervised machine learning models whose scores are combined to create a final score that matches or closely resembles a final score that is computed using all the scores of the multiple unsupervised machine learning models.
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Patent Agency Ranking
6.发明授权公开(公告)号：US11704386B2
公开(公告)日：2023-07-18
申请号：US17199563
申请日：2021-03-12
Applicant: Oracle International Corporation
Inventor： Amin Suzani , Saeid Allahdadian , Milos Vasic , Matteo Casserini , Hamed Ahmadi , Felix Schmidt , Andrew Brownsword , Nipun Agarwal
IPC: G06F18/214 , G06N20/00 , G06V10/75 , G06F18/23
CPC classification number: G06F18/214 , G06F18/23 , G06N20/00 , G06V10/758
Abstract: Herein are feature extraction mechanisms that receive parsed log messages as inputs and transform them into numerical feature vectors for machine learning models (MLMs). In an embodiment, a computer extracts fields from a log message. Each field specifies a name, a text value, and a type. For each field, a field transformer for the field is dynamically selected based the field's name and/or the field's type. The field transformer converts the field's text value into a value of the field's type. A feature encoder for the value of the field's type is dynamically selected based on the field's type and/or a range of the field's values that occur in a training corpus of an MLM. From the feature encoder, an encoding of the value of the field's typed is stored into a feature vector. Based on the MLM and the feature vector, the log message is detected as anomalous.
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公开(公告)号：US20220188694A1
公开(公告)日：2022-06-16
申请号：US17122401
申请日：2020-12-15
Applicant: Oracle International Corporation
Inventor： Amin Suzani , Matteo Casserini , Milos Vasic , Saeid Allahdadian , Andrew Brownsword , Hamed Ahmadi , Felix Schmidt , Nipun Agarwal
Abstract: Approaches herein relate to model decay of an anomaly detector due to concept drift. Herein are machine learning techniques for dynamically self-tuning an anomaly score threshold. In an embodiment in a production environment, a computer receives an item in a stream of items. A machine learning (ML) model hosted by the computer infers by calculation an anomaly score for the item. Whether the item is anomalous or not is decided based on the anomaly score and an adaptive anomaly threshold that dynamically fluctuates. A moving standard deviation of anomaly scores is adjusted based on a moving average of anomaly scores. The moving average of anomaly scores is then adjusted based on the anomaly score. The adaptive anomaly threshold is then adjusted based on the moving average of anomaly scores and the moving standard deviation of anomaly scores.
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公开(公告)号：US12143408B2
公开(公告)日：2024-11-12
申请号：US17739968
申请日：2022-05-09
Applicant: Oracle International Corporation
Inventor： Milos Vasic , Saeid Allahdadian , Matteo Casserini , Felix Schmidt , Andrew Brownsword
Abstract: Techniques for implementing a semi-supervised framework for purpose-oriented anomaly detection are provided. In one technique, a data item in inputted into an unsupervised anomaly detection model, which generates first output. Based on the first output, it is determined whether the data item represents an anomaly. In response to determining that the data item represents an anomaly, the data item is inputted into a supervised classification model, which generates second output that indicates whether the data item is unknown. In response to determining that the data item is unknown, a training instance is generated based on the data item. The supervised classification model is updated based on the training instance.
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公开(公告)号：US20230362180A1
公开(公告)日：2023-11-09
申请号：US17739968
申请日：2022-05-09
Applicant: Oracle International Corporation
Inventor： Milos Vasic , Saeid Allahdadian , Matteo Casserini , Felix Schmidt , Andrew Brownsword
CPC classification number: H04L63/1425 , G06N20/20
Abstract: Techniques for implementing a semi-supervised framework for purpose-oriented anomaly detection are provided. In one technique, a data item in inputted into an unsupervised anomaly detection model, which generates first output. Based on the first output, it is determined whether the data item represents an anomaly. In response to determining that the data item represents an anomaly, the data item is inputted into a supervised classification model, which generates second output that indicates whether the data item is unknown. In response to determining that the data item is unknown, a training instance is generated based on the data item. The supervised classification model is updated based on the training instance.
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公开(公告)号：US11451565B2
公开(公告)日：2022-09-20
申请号：US16122664
申请日：2018-09-05
Applicant: Oracle International Corporation
Inventor： Guang-Tong Zhou , Hossein Hajimirsadeghi , Andrew Brownsword , Stuart Wray , Craig Schelp , Rod Reddekopp , Felix Schmidt
Abstract: 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.
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