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    PATH-BASED PROGRAM LINEAGE INFERENCE ANALYSIS
    11.
    发明申请
    PATH-BASED PROGRAM LINEAGE INFERENCE ANALYSIS 审中-公开

    公开(公告)号：US20190050562A1

    公开(公告)日：2019-02-14

    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 ,  G06F21/56 ,  H04L29/06 ,  G06F21/52 ,  G06F17/30

    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.

    Dynamic border line tracing for tracking message flows across distributed systems
    16.
    发明授权
    Dynamic border line tracing for tracking message flows across distributed systems 有权
    用于跟踪跨分布式系统的消息流的动态边界线跟踪

    公开(公告)号：US09535814B2

    公开(公告)日：2017-01-03

    申请号：US14665519

    申请日：2015-03-23

    Applicant: NEC Laboratories America, Inc.

    Inventor： Nipun Arora ,  Junghwan Rhee ,  Hui Zhang ,  Guofei Jiang

    IPC: G06F9/44 ,  G06F11/34

    CPC classification number: G06F11/3466

    Abstract: The present invention enables capturing API level calls using a combination of dynamic instrumentation and library overriding. The invention allows event level tracing of API function calls and returns, and is able to generate an execution trace. The instrumentation is lightweight and relies on dynamic library/shared library linking mechanisms in most operating systems. Hence we need no source code modification or binary injection. The tool can be used to capture parameter values, and return values, which can be used to correlate traces across API function calls to generate transaction flow logic.

    Abstract translation: 本发明可以使用动态检测和库重写的组合捕获API级别调用。 本发明允许API函数调用和返回的事件级别跟踪，并且能够生成执行跟踪。 该仪器是轻量级的，并且依赖于大多数操作系统中的动态库/共享库链接机制。 因此，我们不需要源代码修改或二进制注入。 该工具可用于捕获参数值和返回值，可用于将API函数调用之间的跟踪相关联，以生成事务流逻辑。

    Anomaly detection with graph adversarial training in computer systems
    18.
    发明授权
    Anomaly detection with graph adversarial training in computer systems 有权

    公开(公告)号：US11606389B2

    公开(公告)日：2023-03-14

    申请号：US17004752

    申请日：2020-08-27

    Applicant: NEC Laboratories America, Inc.

    Inventor： Zhengzhang Chen ,  Jiaping Gui ,  Haifeng Chen ,  Junghwan Rhee ,  Shen Wang

    IPC: H04L29/06 ,  H04L9/40 ,  G06N3/08

    Abstract: Methods and systems for detecting and responding to an intrusion in a computer network include generating an adversarial training data set that includes original samples and adversarial samples, by perturbing one or more of the original samples with an integrated gradient attack to generate the adversarial samples. The original and adversarial samples are encoded to generate respective original and adversarial graph representations, based on node neighborhood aggregation. A graph-based neural network is trained to detect anomalous activity in a computer network, using the adversarial training data set. A security action is performed responsive to the detected anomalous activity.

    Confidential machine learning with program compartmentalization
    19.
    发明授权
    Confidential machine learning with program compartmentalization 有权

    公开(公告)号：US11423142B2

    公开(公告)日：2022-08-23

    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 ,  G06F8/41 ,  G06F21/53 ,  G06F8/75

    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.

    SECURING SOFTWARE INSTALLATION THROUGH DEEP GRAPH LEARNING
    20.
    发明申请
    SECURING SOFTWARE INSTALLATION THROUGH DEEP GRAPH LEARNING 有权

    公开(公告)号：US20210048994A1

    公开(公告)日：2021-02-18

    申请号：US16985647

    申请日：2020-08-05

    Applicant: NEC Laboratories America, Inc.

    Inventor： Xiao Yu ,  Xueyuan Han ,  Ding Li ,  Junghwan Rhee ,  Haifeng Chen

    IPC: G06F8/61 ,  G06N3/04 ,  G06F16/901

    Abstract: A computer-implemented method for securing software installation through deep graph learning includes extracting a new software installation graph (SIG) corresponding to a new software installation based on installation data associated with the new software installation, using at least two node embedding models to generate a first vector representation by embedding the nodes of the new SIG and inferring any embeddings for out-of-vocabulary (OOV) words corresponding to unseen pathnames, utilizing a deep graph autoencoder to reconstruct nodes of the new SIG from latent vector representations encoded by the graph LSTM, wherein reconstruction losses resulting from a difference of a second vector representation generated by the deep graph autoencoder and the first vector representation represent anomaly scores for each node, and performing anomaly detection by comparing an overall anomaly score of the anomaly scores to a threshold of normal software installation.

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