Invention Grant
- Patent Title: Context-aware feature embedding and anomaly detection of sequential log data using deep recurrent neural networks
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Application No.: US16122505Application Date: 2018-09-05
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Publication No.: US11218498B2Publication Date: 2022-01-04
- Inventor: Hossein Hajimirsadeghi , Guang-Tong Zhou , Andrew Brownsword , Nipun Agarwal , Pavan Chandrashekar , Karoon Rashedi Nia
- Applicant: Oracle International Corporation
- Applicant Address: US CA Redwood Shores
- Assignee: Oracle International Corporation
- Current Assignee: Oracle International Corporation
- Current Assignee Address: US CA Redwood Shores
- Agency: Hickman Becker Bingham Ledesma LLP
- Agent Brian N. Miller
- Main IPC: H04L29/06
- IPC: H04L29/06 ; G06N3/02 ; H04L12/24 ; 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.
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