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    Graph-based Instrusion Detection Using Process Traces
    83.
    发明申请
    Graph-based Instrusion Detection Using Process Traces 审中-公开
    使用过程跟踪的基于图形的入侵检测

    公开(公告)号：US20160330226A1

    公开(公告)日：2016-11-10

    申请号：US15213896

    申请日：2016-07-19

    Applicant: NEC Laboratories America, Inc.

    Inventor： Zhengzhang Chen ,  LuAn Tang ,  Boxiang Dong ,  Guofei Jiang ,  Haifeng Chen

    IPC: H04L29/06 ,  H04L12/24

    CPC classification number: H04L63/14 ,  G06F21/55 ,  H04L29/06877 ,  H04L29/06891 ,  H04L29/06911 ,  H04L41/12 ,  H04L41/142 ,  H04L63/1416 ,  H04L2463/121

    Abstract: Methods and systems for detecting malicious processes include modeling system data as a graph comprising vertices that represent system entities and edges that represent events between respective system entities. Each edge has one or more timestamps corresponding respective events between two system entities. A set of valid path patterns that relate to potential attacks is generated. One or more event sequences in the system are determined to be suspicious based on the graph and the valid path patterns using a random walk on the graph.

    Abstract translation: 用于检测恶意进程的方法和系统包括将系统数据建模为包括表示系统实体的顶点和表示各个系统实体之间的事件的边的图。 每个边缘具有对应于两个系统实体之间的相应事件的一个或多个时间戳。 产生一组与潜在攻击有关的有效路径模式。 系统中的一个或多个事件序列被确定为可疑的基于图和有效的路径模式使用图形上的随机游走。

    ADVERSARIAL IMITATION LEARNING ENGINE FOR KPI OPTIMIZATION
    84.
    发明申请
    ADVERSARIAL IMITATION LEARNING ENGINE FOR KPI OPTIMIZATION 有权

    公开(公告)号：US20250149133A1

    公开(公告)日：2025-05-08

    申请号：US18922837

    申请日：2024-10-22

    Applicant: NEC Laboratories America, Inc.

    Inventor： LuAn Tang ,  Yuyang Ye ,  Haifeng Chen ,  Haoyu Wang ,  Zhengzhang Chen ,  Wenchao Yu

    IPC: G16H10/60 ,  G06N3/0455

    Abstract: Systems and methods for optimizing key performance indicators (KPIs) using adversarial imitation deep learning include processing sensor data received from sensors to remove irrelevant data based on correlation to a final KPI and generating, using a policy generator network with a transformer-based architecture, an optimal sequence of actions based on the processed sensor data. A discriminator network is employed to differentiate between the generated action sequences and real-world high performance sequences employing. Final KPI results are estimated based on the generated action sequences using a performance prediction network. The generated action sequences are applied to the process to optimize the KPI in real-time.

    AGENT-BASED CARBON EMISSION REDUCTION SYSTEM
    85.
    发明申请
    AGENT-BASED CARBON EMISSION REDUCTION SYSTEM 有权

    公开(公告)号：US20250148431A1

    公开(公告)日：2025-05-08

    申请号：US18938823

    申请日：2024-11-06

    Applicant: NEC Laboratories America, Inc.

    Inventor： Haoyu Wang ,  Christopher A. White ,  Haifeng Chen ,  LuAn Tang ,  Zhengzhang Chen ,  Xujiang Zhao

    IPC: G06Q10/30 ,  G06Q10/0637

    Abstract: Systems and methods for an agent-based carbon emission reduction system. A carbon product of a supply chain system can be limited below a carbon product threshold by performing a corrective action to monitored entities based on a calculated carbon emission. The carbon emission can be calculated based on carbon-relevant data and a calculation route by utilizing an agent-based simulation model that simulates a learned relationship between a supply chain system and the carbon-relevant data. The calculation route can be determined based on the carbon-relevant data based on a relevance of a carbon product contribution of monitored entities to a goal of the monitored entities. Carbon-relevant data can be extracted from the monitored entities.

    Data fusion and analysis engine for vehicle sensors
    86.
    发明授权
    Data fusion and analysis engine for vehicle sensors 有权

    公开(公告)号：US12263849B2

    公开(公告)日：2025-04-01

    申请号：US17961169

    申请日：2022-10-06

    Applicant: NEC Laboratories America, Inc. ,  NEC Corporation

    Inventor： LuAn Tang ,  Yuncong Chen ,  Wei Cheng ,  Zhengzhang Chen ,  Haifeng Chen ,  Yuji Kobayashi ,  Yuxiang Ren

    IPC: B60W40/09 ,  G06F18/25 ,  G06N3/084

    Abstract: Systems and methods for data fusion and analysis of vehicle sensor data, including receiving a multiple modality input data stream from a plurality of different types of vehicle sensors, determining latent features by extracting modality-specific features from the input data stream, and aligning a distribution of the latent features of different modalities by feature-level data fusion. Classification probabilities can be determined for the latent features using a fused modality scene classifier. A tree-organized neural network can be trained to determine path probabilities and issue driving pattern judgments, with the tree-organized neural network including a soft tree model and a hard decision leaf. One or more driving pattern judgments can be issued based on a probability of possible driving patterns derived from the modality-specific features.

    Vehicle intelligence tool for early warning with fault signature
    87.
    发明授权
    Vehicle intelligence tool for early warning with fault signature 有权

    公开(公告)号：US12205418B2

    公开(公告)日：2025-01-21

    申请号：US17464056

    申请日：2021-09-01

    Applicant: NEC Laboratories America, Inc. ,  NEC Corporation

    Inventor： Luan Tang ,  Wei Cheng ,  Haifeng Chen ,  Yuji Kobayashi ,  Zhengzhang Chen

    IPC: G07C5/08 ,  G06N3/04

    Abstract: A method for early warning is provided. The method clusters normal historical data of normal cars into groups based on the car subsystem to which they belong. The method extracts (i) features based on group membership and (ii) feature correlations based on correlation graphs formed from the groups. The method trains an Auto-Encoder and Auto Decoder (AE&AD) model based on the features and the feature correlations to reconstruct the normal historical data with minimum reconstruction errors. The method reconstructs, using the trained AE&AD model, historical data of specific car fault types with reconstruction errors, normalizes the reconstruction errors, and selects features of the car faults with a top k large errors as fault signatures. The method reconstructs streaming data of monitored cars using the trained AE&AD model to determine streaming reconstruction errors, comparing the streaming reconstruction errors with the fault signatures to predict and provide alerts for impending known faults.

    ZERO-SHOT DOMAIN GENERALIZATION WITH PRIOR KNOWLEDGE
    88.
    发明公开
    ZERO-SHOT DOMAIN GENERALIZATION WITH PRIOR KNOWLEDGE 审中-公开

    公开(公告)号：US20240062043A1

    公开(公告)日：2024-02-22

    申请号：US18364746

    申请日：2023-08-03

    Applicant: NEC Laboratories America, Inc.

    Inventor： Liang Tong ,  Zhengzhang Chen ,  Wei Cheng ,  Haifeng Chen ,  Zhuohang Li

    IPC: G06N3/0455 ,  G06N3/08

    CPC classification number: G06N3/0455 ,  G06N3/08

    Abstract: A computer-implemented method for employing a graph-based adaptive domain generation framework is provided. The method includes, in a training phase, performing domain prototypical network training on source domains, constructing an autoencoding domain relation graph by applying a graph autoencoder to produce domain node embeddings, and performing, via a domain-adaptive classifier, domain-adaptive classifier training to make an informed decision. The method further includes, in a testing phase, given testing samples from a new source domain, computing a prototype by using a pretrained domain prototypical network, inferring node embedding, and making a prediction by the domain-adaptive classifier based on the domain node embeddings.

    INTERPRETING CONVOLUTIONAL SEQUENCE MODEL BY LEARNING LOCAL AND RESOLUTION-CONTROLLABLE PROTOTYPES
    90.
    发明公开
    INTERPRETING CONVOLUTIONAL SEQUENCE MODEL BY LEARNING LOCAL AND RESOLUTION-CONTROLLABLE PROTOTYPES 审中-公开

    公开(公告)号：US20240037397A1

    公开(公告)日：2024-02-01

    申请号：US18479385

    申请日：2023-10-02

    Applicant: NEC Laboratories America, Inc.

    Inventor： Jingchao Ni ,  Zhengzhang Chen ,  Wei Cheng ,  Bo Zong ,  Haifeng Chen

    IPC: G06N3/08 ,  G06N3/04

    CPC classification number: G06N3/08 ,  G06N3/04

    Abstract: A method interprets a convolutional sequence model. The method converts an input data sequence having input segments into output features. The method clusters the input segments into clusters using respective resolution-controllable class prototypes allocated to each of classes. Each respective class prototype includes a respective output feature subset characterizing a respective associated class. The method calculates, using the clusters, similarity scores that indicate a similarity of an output feature to a respective class prototypes responsive to distances between the output feature and the respective class prototypes. The method concatenates the similarity scores to obtain a similarity vector. The method performs a prediction and prediction support operation that provides a value of prediction and an interpretation for the value responsive to the input segments and similarity vector. The interpretation for the value of prediction is provided using only non-negative weights and lacking a weight bias in the fully connected layer.

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