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

IPC: G06F9/54 ,  G06F9/48 ,  G06F11/30 ,  G06F9/38 ,  G06F9/455 ,  G06N20/00 ,  G06F16/11 ,  G06F16/18

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.

公开(公告)号：US11784964B2

公开(公告)日：2023-10-10

申请号：US17197375

申请日：2021-03-10

Applicant: Oracle International Corporation

Inventor： Renata Khasanova ,  Felix Schmidt ,  Stuart Wray ,  Craig Schelp ,  Nipun Agarwal ,  Matteo Casserini

IPC: H04L61/4511 ,  G06N20/00 ,  H04L41/16 ,  G06F40/30

CPC classification number: H04L61/4511 ,  G06N20/00 ,  H04L41/16 ,  G06F40/30

Abstract: Techniques are described herein for using machine learning to learn vector representations of DNS requests such that the resulting embeddings represent the semantics of the DNS requests as a whole. Techniques described herein perform pre-processing of tokenized DNS request strings in which hashes, which are long and relatively random strings of characters, are detected in DNS request strings and each detected hash token is replaced with a placeholder token. A vectorizing ML model is trained using the pre-processed training dataset in which hash tokens have been replaced. Embeddings for the DNS tokens are derived from an intermediate layer of the vectorizing ML model. The encoding application creates final vector representations for each DNS request string by generating a weighted summation of the embeddings of all of the tokens in the DNS request string. Because of hash replacement, the resulting DNS request embeddings reflect semantics of the hashes as a group.

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

IPC: H04L29/06 ,  G06N3/04 ,  G06K9/62 ,  G06F16/80

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.

公开(公告)号：US10892961B2

公开(公告)日：2021-01-12

申请号：US16271535

申请日：2019-02-08

Applicant: Oracle International Corporation

Inventor： Onur Kocberber ,  Felix Schmidt ,  Craig Schelp ,  Pravin Shinde

IPC: H04L12/24 ,  H04L12/26 ,  G06F9/455 ,  H04L29/08

Abstract: Herein are computerized techniques for autonomous and artificially intelligent administration of a computer cloud health monitoring system. In an embodiment, an orchestration computer automatically detects a current state of network elements of a computer network by processing: a) a network plan that defines a topology of the computer network, and b) performance statistics of the network elements. The network elements include computers that each hosts virtual execution environment(s). Each virtual execution environment hosts analysis logic that transforms raw performance data of a network element into a portion of the performance statistics. For each computer, a configuration specification for each virtual execution environment of the computer is automatically generated based on the network plan and the current state of the computer network. At least one virtual execution environment is automatically tuned and/or re-provisioned based on a generated configuration specification.

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

IPC: G06N3/04 ,  H04L9/40 ,  G06K9/62

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.

公开(公告)号：US11416324B2

公开(公告)日：2022-08-16

申请号：US15930779

申请日：2020-05-13

Applicant: Oracle International Corporation

Inventor： Paria Rashidinejad ,  Navaneeth Jamadagni ,  Arun Raghavan ,  Craig Schelp ,  Charles Gordon

IPC: G06F11/07

Abstract: Techniques are described herein for accurately measuring the reliability of storage systems. Rather than relying on a series of approximations, which may produce highly optimistic estimates, the techniques described herein use a failure distribution derived from a disk failure data set to derive reliability metrics such as mean time to data loss (MTTDL) and annual durability. A new framework for modeling storage system dynamics is described herein. The framework facilitates theoretical analysis of the reliability. The model described herein captures the complex structure of storage systems considering their configuration, dynamics, and operation. Given this model, a simulation-free analytical solution to the commonly used reliability metrics is derived. The model may also be used to analyze the long-term reliability behavior of storage systems.

公开(公告)号：US20200371855A1

公开(公告)日：2020-11-26

申请号：US15930779

申请日：2020-05-13

Applicant: Oracle International Corporation

Inventor： Paria Rashidinejad ,  Navaneeth Jamadagni ,  Arun Raghavan ,  Craig Schelp ,  Charles Gordon

IPC: G06F11/07

Abstract: Techniques are described herein for accurately measuring the reliability of storage systems. Rather than relying on a series of approximations, which may produce highly optimistic estimates, the techniques described herein use a failure distribution derived from a disk failure data set to derive reliability metrics such as mean time to data loss (MTTDL) and annual durability. A new framework for modeling storage system dynamics is described herein. The framework facilitates theoretical analysis of the reliability. The model described herein captures the complex structure of storage systems considering their configuration, dynamics, and operation. Given this model, a simulation-free analytical solution to the commonly used reliability metrics is derived. The model may also be used to analyze the long-term reliability behavior of storage systems.

公开(公告)号：US20200259722A1

公开(公告)日：2020-08-13

申请号：US16271535

申请日：2019-02-08

Applicant: Oracle International Corporation

Inventor： Onur Kocberber ,  Felix Schmidt ,  Craig Schelp ,  Pravin Shinde

IPC: H04L12/24 ,  H04L12/26 ,  G06F9/455

Abstract: Herein are computerized techniques for autonomous and artificially intelligent administration of a computer cloud health monitoring system. In an embodiment, an orchestration computer automatically detects a current state of network elements of a computer network by processing: a) a network plan that defines a topology of the computer network, and b) performance statistics of the network elements. The network elements include computers that each hosts virtual execution environment(s). Each virtual execution environment hosts analysis logic that transforms raw performance data of a network element into a portion of the performance statistics. For each computer, a configuration specification for each virtual execution environment of the computer is automatically generated based on the network plan and the current state of the computer network. At least one virtual execution environment is automatically tuned and/or re-provisioned based on a generated configuration specification.

公开(公告)号：US20230421528A1

公开(公告)日：2023-12-28

申请号：US18237853

申请日：2023-08-24

Applicant: Oracle International Corporation

Inventor： Renata Khasanova ,  Felix Schmidt ,  Stuart Wray ,  Craig Schelp ,  Nipun Agarwal ,  Matteo Casserini

IPC: H04L61/4511 ,  G06N20/00 ,  H04L41/16

CPC classification number: H04L61/4511 ,  G06F40/30 ,  H04L41/16 ,  G06N20/00

Abstract: Techniques are described herein for using machine learning to learn vector representations of DNS requests such that the resulting embeddings represent the semantics of the DNS requests as a whole. Techniques described herein perform pre-processing of tokenized DNS request strings in which hashes, which are long and relatively random strings of characters, are detected in DNS request strings and each detected hash token is replaced with a placeholder token. A vectorizing ML model is trained using the pre-processed training dataset in which hash tokens have been replaced. Embeddings for the DNS tokens are derived from an intermediate layer of the vectorizing ML model. The encoding application creates final vector representations for each DNS request string by generating a weighted summation of the embeddings of all of the tokens in the DNS request string. Because of hash replacement, the resulting DNS request embeddings reflect semantics of the hashes as a group.

公开(公告)号：US20220294757A1

公开(公告)日：2022-09-15

申请号：US17197375

申请日：2021-03-10

Applicant: Oracle International Corporation

Inventor： Renata Khasanova ,  Felix Schmidt ,  Stuart Wray ,  Craig Schelp ,  Nipun Agarwal ,  Matteo Casserini

IPC: H04L29/12 ,  H04L12/24 ,  G06N20/00

Abstract: Techniques are described herein for using machine learning to learn vector representations of DNS requests such that the resulting embeddings represent the semantics of the DNS requests as a whole. Techniques described herein perform pre-processing of tokenized DNS request strings in which hashes, which are long and relatively random strings of characters, are detected in DNS request strings and each detected hash token is replaced with a placeholder token. A vectorizing ML model is trained using the pre-processed training dataset in which hash tokens have been replaced. Embeddings for the DNS tokens are derived from an intermediate layer of the vectorizing ML model. The encoding application creates final vector representations for each DNS request string by generating a weighted summation of the embeddings of all of the tokens in the DNS request string. Because of hash replacement, the resulting DNS request embeddings reflect semantics of the hashes as a group.