Invention Grant
- Patent Title: Malicious activity detection by cross-trace analysis and deep learning
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Application No.: US16122664Application Date: 2018-09-05
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Publication No.: US11451565B2Publication Date: 2022-09-20
- Inventor: Guang-Tong Zhou , Hossein Hajimirsadeghi , Andrew Brownsword , Stuart Wray , Craig Schelp , Rod Reddekopp , Felix Schmidt
- 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: G06N3/04
- 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.
Public/Granted literature
- US20200076842A1 MALICIOUS ACTIVITY DETECTION BY CROSS-TRACE ANALYSIS AND DEEP LEARNING Public/Granted day:2020-03-05
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