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    Real-time threat alert forensic analysis
    1.
    发明授权
    Real-time threat alert forensic analysis 有权

    公开(公告)号：US11275832B2

    公开(公告)日：2022-03-15

    申请号：US16781366

    申请日：2020-02-04

    Applicant: NEC Laboratories America, Inc.

    Inventor： Ding Li ,  Kangkook Jee ,  Zhichun Li ,  Zhengzhang Chen ,  Xiao Yu

    IPC: G06F21/55 ,  G06F21/56

    Abstract: Methods and systems for security monitoring and response include assigning an anomaly score to each of a plurality of event paths that are stored in a first memory. Events that are cold, events that are older than a threshold, and events that are not part of a top-k anomalous path are identified. The identified events are evicted from the first memory to a second memory. A threat associated with events in the first memory is identified. A security action is performed responsive to the identified threat.

    Path-based program lineage inference analysis
    2.
    发明授权
    Path-based program lineage inference analysis 有权

    公开(公告)号：US10853487B2

    公开(公告)日：2020-12-01

    申请号：US16039993

    申请日：2018-07-19

    Applicant: NEC Laboratories America, Inc. ,  NEC Corporation

    Inventor： Junghwan Rhee ,  Zhenyu Wu ,  Lauri Korts-Parn ,  Kangkook Jee ,  Zhichun Li ,  Omid Setayeshfar

    IPC: G06F21/55 ,  G06F16/21 ,  G06F16/26 ,  G06F16/901 ,  G06F21/52 ,  G06F21/56 ,  H04L29/06

    Abstract: Systems and methods are disclosed for securing an enterprise environment by detecting suspicious software. A global program lineage graph is constructed. Construction of the global program lineage graph includes creating a node for each version of a program having been installed on a set of user machines. Additionally, at least two nodes are linked with a directional edge. For each version of the program, a prevalence number of the set of user machines on which each version of the program had been installed is determined; and the prevalence number is recorded to the metadata associated with the respective node. Anomalous behavior is identified based on structures formed by the at least two nodes and associated directional edge in the global program lineage graph. An alarm is displayed on a graphical user interface for each suspicious software based on the identified anomalous behavior.

    TEMPLATE BASED DATA REDUCTION FOR COMMERCIAL DATA MINING
    5.
    发明申请
    TEMPLATE BASED DATA REDUCTION FOR COMMERCIAL DATA MINING 审中-公开

    公开(公告)号：US20180336218A1

    公开(公告)日：2018-11-22

    申请号：US15979514

    申请日：2018-05-15

    Applicant: NEC Laboratories America, Inc.

    Inventor： Ding Li ,  Kangkook Jee ,  Zhichun Li ,  Mu Zhang ,  Zhenyu Wu

    IPC: G06F17/30 ,  G06F3/06 ,  G06K9/62

    Abstract: Systems and methods for mining and compressing commercial data including a network of point of sale devices to log commercial activity data including independent commercial events and corresponding dependent features. A middleware system is in communication with the network of point of sale devices to continuously collect and compress a stream of the commercial activity data and concurrently store the compressed commercial activity data. Compressing the stream includes a file access table corresponding to the commercial activity data, producing compressible file access templates (CFATs) according to frequent patterns of commercial activity data using the file access table, and replacing dependent feature sequences with a matching compressible file access template. A database is in communication with the middleware system to store the compressed commercial data. A commercial pattern analysis system is in communication with the database to determine patterns in commercial activities across the network of point of sale devices.

    GRAPHICS PROCESSING UNIT ACCELERATED TRUSTED EXECUTION ENVIRONMENT
    7.
    发明申请
    GRAPHICS PROCESSING UNIT ACCELERATED TRUSTED EXECUTION ENVIRONMENT 审中-公开

    公开(公告)号：US20200257794A1

    公开(公告)日：2020-08-13

    申请号：US16787610

    申请日：2020-02-11

    Applicant: NEC Laboratories America, Inc.

    Inventor： Chung Hwan Kim ,  Junghwan Rhee ,  Kangkook Jee ,  Zhichun Li ,  Adil Ahmad ,  Haifeng Chen

    IPC: G06F21/53 ,  G06F21/60 ,  G06F21/12 ,  G06F9/455 ,  G06F21/44 ,  G06F21/54

    Abstract: Systems and methods for implementing a system architecture to support a trusted execution environment (TEE) with computational acceleration are provided. The method includes establishing a first trusted channel between a user application stored on an enclave and a graphics processing unit (GPU) driver loaded on a hypervisor. Establishing the first trusted channel includes leveraging page permissions in an extended page table (EPT) to isolate the first trusted channel between the enclave and the GPU driver in a physical memory of an operating system (OS). The method further includes establishing a second trusted channel between the GPU driver and a GPU device. The method also includes launching a unified TEE that includes the enclave and the hypervisor with execution of application code of the user application.

    CONFIDENTIAL MACHINE LEARNING WITH PROGRAM COMPARTMENTALIZATION
    8.
    发明申请
    CONFIDENTIAL MACHINE LEARNING WITH PROGRAM COMPARTMENTALIZATION 审中-公开

    公开(公告)号：US20200184070A1

    公开(公告)日：2020-06-11

    申请号：US16693710

    申请日：2019-11-25

    Applicant: NEC Laboratories America, Inc.

    Inventor： Chung Hwan Kim ,  Junghwan Rhee ,  Kangkook Jee ,  Zhichun Li

    IPC: G06F21/54 ,  G06N20/00 ,  G06F8/73 ,  G06F8/75 ,  G06F8/41 ,  G06F21/53

    Abstract: A method for implementing confidential machine learning with program compartmentalization includes implementing a development stage to design an ML program, including annotating source code of the ML program to generate an ML program annotation, performing program analysis based on the development stage, including compiling the source code of the ML program based on the ML program annotation, inserting binary code based on the program analysis, including inserting run-time code into a confidential part of the ML program and a non-confidential part of the ML program, and generating an ML model by executing the ML program with the inserted binary code to protect the confidentiality of the ML model and the ML program from attack.

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