公开(公告)号：US20250131154A1

公开(公告)日：2025-04-24

申请号：US18619802

申请日：2024-03-28

Applicant: NEC Laboratories America, Inc.

Inventor： LuAn Tang ,  Peng Yuan ,  Haifeng Chen

IPC: G06F30/20

Abstract: Systems and methods for creating a model include converting historical data into categorical time series data; de-noising the categorical time series data by organizing events into transition sets and removing noisy transitions sets according to a coefficient of variation. A relationship graph is generated that determines relationships between pairs of nodes, where the nodes relate to respective data sources and where the relationships indicate a degree of correlation between nodes based on the de-noised categorical time-series data, using a Hawkes process that determines a likelihood of a category transition based on historical events. An anomaly threshold is determined based on anomaly scores for a validation dataset using the relationship graph, wherein a likelihood output of the Hawkes process that exceeds the anomaly threshold indicates an anomaly.

公开(公告)号：US20240354215A1

公开(公告)日：2024-10-24

申请号：US18594582

申请日：2024-03-04

Applicant: NEC Laboratories America, Inc.

Inventor： Peng Yuan ,  LuAn Tang ,  Haifeng Chen ,  Yuncong Chen ,  Zhengzhang Chen ,  Motoyuki Sato

IPC: G06F11/34 ,  G06F11/32

CPC classification number: G06F11/3452 ,  G06F11/327

Abstract: Systems and methods are provided for incident analysis in Cyber-Physical Systems (CPS) using a Temporal Graph-based Incident Analysis System (TGIAS) and/or Transition Based Categorical Anomaly Detection (TCAD). Dynamically gathered multimodal data from a distributed network of sensors across the CPS are preprocessed to identify abnormal sensor readings indicative of potential incidents, and a multi-layered incident timeline graph, representing abnormal sensor readings, relationships to specific CPS components, and temporal sequencing of events is constructed. Severity scores are calculated, and severity rankings are assigned to identified anomalies based on a composite index including impact on CPS operation, comparison with historical incident data, and predictive risk assessments. Probable root causes of incidents and pathways for anomaly propagation through the CPS are identified using causal interference and the incident timeline graph to detect underlying vulnerabilities and predict future system weaknesses. Recommended actions are generated and executed for incident resolution and system optimization.

公开(公告)号：US20250133099A1

公开(公告)日：2025-04-24

申请号：US18829972

申请日：2024-09-10

Applicant: NEC Laboratories America, Inc.

Inventor： Peng Yuan ,  LuAn Tang ,  Haifeng Chen

IPC: H04L9/40 ,  G06F16/25 ,  G06F16/28

Abstract: Systems and methods include converting historical data into categorical time series data and de-noising the categorical time series data by removing noisy transitions sets according to a coefficient of variation. A likelihood of a category transition is determined based on historical events using a Hawkes process to generate a relationship graph. Relationships between pairs of nodes are determined using the relationship graph, where the relationships indicate a degree of correlation between the nodes based on de-noised categorical time-series data. An anomaly threshold is determined based on anomaly scores for a validation dataset using the relationship graph, wherein a likelihood output of the Hawkes process that exceeds the anomaly threshold indicates an anomaly.

公开(公告)号：US20250131509A1

公开(公告)日：2025-04-24

申请号：US18619851

申请日：2024-03-28

Applicant: NEC Laboratories America, Inc.

Inventor： LuAn Tang ,  Peng Yuan ,  Haifeng Chen

IPC: G06Q40/08

Abstract: Systems and methods for event prediction include converting event information into categorical time series data for a plurality of properties; determining relationships between pairs of properties of the plurality of properties based on a plurality of data types. A likelihood of an event occurring is predicted during a future period by summing over a Hawkes process for the pairs of properties, the Hawkes process taking as input the relationships between the pairs of properties and comparing the likelihood to an anomaly threshold that is based on a range of normal intensity values for transition sets based on the categorical time series data; and performing an action responsive to a determination that the likelihood exceeds the anomaly threshold.

公开(公告)号：US20240354184A1

公开(公告)日：2024-10-24

申请号：US18594487

申请日：2024-03-04

Applicant: NEC Laboratories America, Inc.

Inventor： Peng Yuan ,  LuAn Tang ,  Haifeng Chen ,  Yuncong Chen ,  Zhengzhang Chen ,  Motoyuki Sato

IPC: G06F11/07

CPC classification number: G06F11/079 ,  G06F11/0736 ,  G06F11/0793

Abstract: Systems and methods are provided for incident analysis in Cyber-Physical Systems (CPS) using a Temporal Graph-based Incident Analysis System (TGIAS) and/or Transition Based Categorical Anomaly Detection (TCAD). Dynamically gathered multimodal data from a distributed network of sensors across the CPS are preprocessed to identify abnormal sensor readings indicative of potential incidents, and a multi-layered incident timeline graph, representing abnormal sensor readings, relationships to specific CPS components, and temporal sequencing of events is constructed. Severity scores are calculated, and severity rankings are assigned to identified anomalies based on a composite index including impact on CPS operation, comparison with historical incident data, and predictive risk assessments. Probable root causes of incidents and pathways for anomaly propagation through the CPS are identified using causal interference and the incident timeline graph to detect underlying vulnerabilities and predict future system weaknesses. Recommended actions are generated and executed for incident resolution and system optimization.

公开(公告)号：US20250131296A1

公开(公告)日：2025-04-24

申请号：US18619872

申请日：2024-03-28

Applicant: NEC Laboratories America, Inc.

Inventor： LuAn Tang ,  Peng Yuan ,  Haifeng Chen

IPC: G06N5/048

Abstract: Systems and methods for pre-processing time series data include assigning transition events from categorical time series data into a list of transition sets that each include transitions from a respective first category to a respective second category and determining a mean duration and standard deviation, for each transition set, of the respective first category before the transition to the respective second category. A ratio is compared between the mean duration and the standard deviation to a threshold value to identify noisy transition sets; removing noisy transition sets from the list of transition sets to output de-noised transition sets. A probability of an event occurrence is predicted using the de-noised transition sets, and an action is performed responsive to the probability.

公开(公告)号：US20240086586A1

公开(公告)日：2024-03-14

申请号：US18464381

申请日：2023-09-11

Applicant: NEC Laboratories America, Inc. ,  NEC Corporation

Inventor： LuAn Tang ,  Shepard Jiang ,  Peng Yuan ,  Yuncong Chen ,  Haifeng Chen ,  Yuji Kobayashi

IPC: G06F30/20 ,  G06N3/08

CPC classification number: G06F30/20 ,  G06N3/08

Abstract: A computer-implemented method for simulating vehicle data and improving driving scenario detection is provided. The method includes retrieving, from vehicle sensors, key parameters from real data of validation scenarios to generate corresponding scenario configurations and descriptions, transferring target scenario descriptions and validation scenario descriptions to target scenario scripts and validation scenario scripts, respectively, to create first raw simulation data pertaining to target scenario descriptions and second raw simulation data pertaining to validation scenario descriptions, training, by an adjuster network, a deep neural network model to minimize differences between the first raw simulation data and the second raw simulation data, refining the first and second raw simulation data of rare driving scenarios to generate rare driving scenario training data, and outputting the rare driving scenario training data to a display screen of a computing device to enable a user to train a scenario detector for an autonomic driving assistant system.

公开(公告)号：US20230281186A1

公开(公告)日：2023-09-07

申请号：US18173431

申请日：2023-02-23

Applicant: NEC Laboratories America, Inc.

Inventor： Peng Yuan ,  LuAn Tang ,  Haifeng Chen ,  Motoyuki Sato

IPC: G06F16/23

CPC classification number: G06F16/2365

Abstract: Methods and systems for anomaly correction include detecting an anomaly in a time series of categorical data values generated by a sensor, displaying a visual depiction of an anomalous time series, corresponding to the detected anomaly, on a user interface with a visual depiction of an expected normal behavior to contrast to the anomalous time series, and performing a corrective action responsive to the displayed detected anomaly. Detecting the anomaly includes framing the time series with a sliding window, generating a histogram for the categorical data values using a histogram template, generating an anomaly score for the time series using an anomaly detection histogram model on the generated histogram, and comparing the anomaly score to an anomaly threshold.

公开(公告)号：US20240104344A1

公开(公告)日：2024-03-28

申请号：US18467069

申请日：2023-09-14

Applicant: NEC Laboratories America, Inc. ,  NEC Corporation

Inventor： LuAn Tang ,  Peng Yuan ,  Yuncong Chen ,  Haifeng Chen ,  Yuji Kobayashi ,  Jiafan He

IPC: G06N3/0442

CPC classification number: G06N3/0442

Abstract: Methods and systems for training a model include distinguishing hidden states of a monitored system based on condition information. An encoder and decoder are generated for each respective hidden state using forward and backward autoencoder losses. A hybrid hidden state is determined for an input sequence based on the hidden states. The input sequence is reconstructed using the encoders and decoders and the hybrid hidden state. Parameters of the encoders and decoders are updated based on a reconstruction loss.

公开(公告)号：US20230280739A1

公开(公告)日：2023-09-07

申请号：US18173452

申请日：2023-02-23

Applicant: NEC Laboratories America, Inc.

Inventor： Peng Yuan ,  LuAn Tang ,  Haifeng Chen ,  Motoyuki Sato

IPC: G05B23/02

CPC classification number: G05B23/0283

Abstract: Methods and systems for anomaly detection include training an anomaly detection histogram model using historical categorical value data. Training the anomaly detection histogram model includes generating a histogram template based on historical categorical data, converting the historical categorical data to a histogram using the histogram template, and determining a normal range and anomaly threshold for the categorical data using the histogram.