Abstract: A pseudonym certificate management method, performed by a pseudonym certificate management apparatus interworking with an external server, may comprise: receiving, from the external server, a pseudonym certificate in a state locked based on a root value identifiable only by the external server; periodically receiving an unlocking key for the pseudonym certificate from the external server; activating the pseudonym certificate with the unlocking key; and when the activated pseudonym certificate is abnormal, deactivating the pseudonym certificate.

Abstract: A method of distributedly storing data in a system comprising a plurality of edge nodes communicatively coupled to an end device and a central cloud. The method includes: receiving, by a first edge node, data transmitted by the end device; assigning, by the first edge node, a data tag to received data according to attributes of the received data and duplicating the received data to generate a tagged data copy; and transmitting, by the first edge node, the tagged data copy to at least one second edge node determined by a data distribution policy determined by the central cloud directly or through the central cloud, so that the at least one second edge node store the tagged data copy in a storage.

Abstract: Provided are a method and device for performing authentication using a hardware security module (HSM) in a one machine-to-machine (oneM2M) environment. The method of performing authentication using an HSM in a oneM2M environment includes extracting a symmetric key stored in the HSM using a security application programming interface (API), generating a first value and a second value using the extracted symmetric key, and performing mutual authentication with an M2M enrolment function (MEF) server through transport layer security pre-shared key ciphersuites (TLS-PSK) using the first value and the second value.

Abstract: 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.

Abstract: Provided are a device and method for sharing big data using a blockchain. The method of sharing big data using a blockchain includes receiving a search condition from a user terminal, searching a blockchain network for metadata meeting the received search condition and acquiring found metadata, acquiring shared data stored in a distributed storage using the acquired metadata, and transmitting the acquired shared data to the user terminal.

Abstract: A method for managing an enrollment certificate in an SCMS for V2X communication may comprise receiving a message requesting an authority for generating a CSR from a DCM; updating device certification data through a certification laboratory that manages performance and requirements of an EE, and verifying the EE that requested issuance of an enrollment certificate to the DCM by using the updated device certification data; transmitting response data indicating the authority for generating a CSR to the DCM based on a result of the verification; receiving a CSR generated according to the response data from the DCM; and verifying the received CSR, and requesting response data for the CSR to an ECA based on a result of the verification.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Provided are a device and method for sharing big data using a blockchain. The method of sharing big data using a blockchain includes receiving a search condition from a user terminal, searching a blockchain network for metadata meeting the received search condition and acquiring found metadata, acquiring shared data stored in a distributed storage using the acquired metadata, and transmitting the acquired shared data to the user terminal.

Abstract: 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.

Abstract: Provided are a method and device for performing authentication using a hardware security module (HSM) in a one machine-to-machine (oneM2M) environment. The method of performing authentication using an HSM in a oneM2M environment includes extracting a symmetric key stored in the HSM using a security application programming interface (API), generating a first value and a second value using the extracted symmetric key, and performing mutual authentication with an M2M enrolment function (MEF) server through transport layer security pre-shared key ciphersuites (TLS-PSK) using the first value and the second value.

Abstract: 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.

Abstract: A method for managing an enrollment certificate in an SCMS for V2X communication may comprise receiving a message requesting an authority for generating a CSR from a DCM; updating device certification data through a certification laboratory that manages performance and requirements of an EE, and verifying the EE that requested issuance of an enrollment certificate to the DCM by using the updated device certification data; transmitting response data indicating the authority for generating a CSR to the DCM based on a result of the verification; receiving a CSR generated according to the response data from the DCM; and verifying the received CSR, and requesting response data for the CSR to an ECA based on a result of the verification.

Abstract: 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.

Abstract: Provided are an apparatus and method for verifying the integrity of a video file. The method includes receiving a video file and model information of a video recording device which is claimed to have recorded a verification-target video, extracting a header structure and a header value of the video file, extracting characteristic information according to the received model information of the video recording device from a characteristic database, and determining whether the extracted characteristic information according to the model information of the video recording device corresponds to the header structure and the header value of the video file.

Abstract: Provided is a method for detecting an abnormal session including a request message received by a server from a client and a response message generated by the server, the method including transforming at least a part of messages included in the session into data in the form of a matrix, transforming the data in the form of the matrix into a representation vector a dimension of which is lower than a dimension of the matrix of the data using a convolutional neural network, and determining whether the session is abnormal by arranging the representation vectors obtained from the messages in an order in which the messages are generated to compose a first representation vector sequence, and analyzing the first to representation vector sequence using an long short term memory (LSTM) neural network.

Abstract: 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.

Abstract: 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.