Invention Grant
- Patent Title: Unsupervised graph similarity learning based on stochastic subgraph sampling
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Application No.: US17017048Application Date: 2020-09-10
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Publication No.: US11544377B2Publication Date: 2023-01-03
- Inventor: Bo Zong , Haifeng Chen , Lichen Wang
- Applicant: NEC Laboratories America, Inc.
- Applicant Address: US NJ Princeton
- Assignee: NEC Laboratories America, Inc.
- Current Assignee: NEC Laboratories America, Inc.
- Current Assignee Address: US NJ Princeton
- Agent Joseph Kolodka
- Main IPC: G06F21/56
- IPC: G06F21/56 ; G06V10/75 ; G06K9/62 ; G06N3/04

Abstract:
Methods and systems for detecting abnormal application behavior include determining a vector representation of a first syscall graph that is generated by a first application, the vector representation including a representation of a distribution of subgraphs of the first syscall graph. The vector representation of the first syscall graph is compared to one or more second syscall graphs that are generated by respective second applications to determine respective similarity scores. It is determined that the first application is behaving abnormally based on the similarity scores, and a security action is performed responsive to the determination that the first application is behaving abnormally.
Public/Granted literature
- US20210089652A1 UNSUPERVISED GRAPH SIMILARITY LEARNING BASED ON STOCHASTIC SUBGRAPH SAMPLING Public/Granted day:2021-03-25
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