Invention Grant
- Patent Title: Recurrent neural network based anomaly detection
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Application No.: US17693860Application Date: 2022-03-14
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Publication No.: US11775637B2Publication Date: 2023-10-03
- Inventor: Heqing Huang , Taesung Lee , Ian M. Molloy , Zhongshu Gu , Jialong Zhang , Josyula R. Rao
- Applicant: International Business Machines Corporation
- Applicant Address: US NY Armonk
- Assignee: International Business Machines Corporation
- Current Assignee: International Business Machines Corporation
- Current Assignee Address: US NY Armonk
- Agent Stephen J. Walder, Jr.; Anthony M. Pallone
- Main IPC: G06F21/55
- IPC: G06F21/55 ; G06N20/00 ; G06N3/08

Abstract:
Mechanisms are provided for detecting abnormal system call sequences in a monitored computing environment. The mechanisms receive, from a computing system resource of the monitored computing environment, a system call of an observed system call sequence for evaluation. A trained recurrent neural network (RNN), trained to predict system call sequences, processes the system call to generate a prediction of a subsequent system call in a predicted system call sequence. Abnormal call sequence logic compares the subsequent system call in the predicted system call sequence to an observed system call in the observed system call sequence and identifies a difference between the predicted system call sequence and the observed system call sequence based on results of the comparing. The abnormal call sequence logic generates an alert notification in response to identifying the difference.
Public/Granted literature
- US20220207137A1 Recurrent Neural Network Based Anomaly Detection Public/Granted day:2022-06-30
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