Penta Security Systems Inc.

Autocrypt Co., Ltd.

Cloudbric Corp

Penta Security Inc.

Seung Young Park

Seok Lee

Duk Soo Kim

Sang Gyoo Sim

Eui Seok Kim

Myung Jin Kim

Eun Jung Ko

Ki Ho Joo

Tae Joon Jung

Jung Won Lee

Tai Yun Kim

A method of detecting an anomaly of a road side unit, which is performed by an anomaly detecting apparatus interworking with one or more road side units, may comprise: receiving traffic data from each of the one or more road side units; performing grouping on the one or more road side units based on the traffic data; generating input data of an artificial neural network based on a result of the performing of the grouping; generating output data of the artificial neural network based on the input data; and detecting an anomaly of each of the one or more road side units based on the output data.

Method of detecting anomaly of road side unit and apparatus therefor

Provided is a method for detecting a driver's abnormalities based on a CAN (Controller Area Network) bus network communicating with an ECU (Electronic Control Unit) of a vehicle. The method may include: acquiring a CAN bus signal related to an operation of the vehicle from the CAN bus network; extracting a detection vector from the CAN bus signal using an auto encoder; and detecting a driver's abnormality based on the detection vector.

Method and apparatus for detecting driver's abnormalities based on machine learning using vehicle can bus signal

Provided is a deep learning method including a step of each of at least two or more deep learning machines learning a web traffic by using a hexadecimal; a step of the deep learning machines learning the web traffic by using an incremental learning using a weight; a step of, when the web traffic is received, each of the deep learning machines encoding a character string of the web traffic with UTF-8 hexadecimal; a step of each of the deep learning machines converting the character string into an image and deep learning the image.

Deep learning method

An operation method of a communication node in a communication network may comprise receiving CAN signals from a plurality of CAN communication nodes; generating forward input data by performing forward preprocessing on the received CAN signals based on forward sampling times; generating forward mask data for the forward input data and forward time interval data according to the forward sampling times; generating backward input data by performing backward preprocessing on the CAN signals based on backward sampling times; generating backward mask data for the backward input data and backward time interval data according to the backward sampling times; and detecting an abnormal signal among the CAN signals by inputting the forward input data, forward mask data, forward time interval data, backward input data, backward mask data, and backward time interval data into a gated recurrent unit-decay (GRU-D) neural network.

Method for detecting signal in communication network based on controller area network, and apparatus therefor

Disclosed is a method of detecting whether a groupware user's behavior is an abnormal behavior. The method includes acquiring a uniform resource identifier (URI) from the user's behavior, generating a URI sequence from the URI on the basis of a predetermined time interval, preprocessing the URI sequence to generate a primitive URI sequence, calculating a log probability value from the primitive URI sequence, and detecting whether the user's behavior is an abnormal behavior on the basis of the log probability value and a predetermined threshold value.

Method and apparatus for detecting abnormal behavior of groupware user

Provided is a method of machine learning for a convolutional neural network (CNN). The method includes: receiving input target data; determining whether to initiate incremental learning on the basis of a difference between a statistical characteristic of the target data with respect to the CNN and a statistical characteristic of previously used training data with respect to the CNN; determining a set of kernels with a high degree of mutual similarity in each convolution layer included in the CNN when the incremental learning is determined to be initiated; and updating a weight between nodes to which kernels included in the set of kernels with a high degree of mutual similarity are applied.

Method and apparatus for machine learning

Disclosed herein are a method and an apparatus for detecting abnormal traffic based on a convolutional autoencoder (CAE). The method of detecting abnormal traffic based on the CAE may include converting a character string of normal traffic into an image, learning the converted image using the CAE, and detecting abnormal traffic by inputting target traffic to the learned CAE.

Method and apparatus for detecting abnormal traffic based on convolutional autoencoder

Disclosed is a method for detecting anomaly traffic using an autoencoder based on an inception-residual network-v2 which is one type of machine learning methods, the method including generating a plurality of encoders on the basis of traffic data for learning; generating a plurality of pieces of image data on the basis of traffic data for each session, which is a detection target; and determining whether the traffic data for each session is abnormal using at least one among the traffic data for learning, the plurality of encoders, and the plurality of pieces of image data.

Growing community of inventors

Chuncheon-si, South Korea

Seung Young Park