Invention Grant
- Patent Title: Malicious activity detection by cross-trace analysis and deep learning
-
Application No.: US16122398Application Date: 2018-09-05
-
Publication No.: US11082438B2Publication Date: 2021-08-03
- Inventor: Juan Fernandez Peinador , Manel Fernandez Gomez , Guang-Tong Zhou , Hossein Hajimirsadeghi , Andrew Brownsword , Onur Kocberber , Felix Schmidt , Craig Schelp
- 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
- Main IPC: H04L29/06
- 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.
Public/Granted literature
- US20200076840A1 MALICIOUS ACTIVITY DETECTION BY CROSS-TRACE ANALYSIS AND DEEP LEARNING Public/Granted day:2020-03-05
Information query