Abstract: The present invention relates to a method for collecting data from a multi-domain in a data collection device. The method includes a step A of collecting data from a general web that is accessible through a search engine; a step B of collecting data from a dark web site that is not accessible with a general web browser and is accessible with preset specific software; and a step C of standardizing the collected data in a preset format and generating metadata for the collected data.

Abstract: The present invention relates to a method for collecting data from a multi-domain in a data collection device. The method includes a step A of collecting data from a general web that is accessible through a search engine; a step B of collecting data from a dark web site that is not accessible with a general web browser and is accessible with preset specific software; and a step C of standardizing the collected data in a preset format and generating metadata for the collected data.

Abstract: The present disclosure relates to a method and apparatus for acquiring learning data to generate a machine learning model for detecting a scam account of cryptocurrency. The method comprises receiving a report related to a scam address from a first database having information about a reported scam address stored therein, acquiring a first scam address and a first description related to the first scam address from the report, extracting a plurality of first keywords related to the first scam address from the first description using natural language processing, and storing the first scam address in a second database.

Abstract: The present disclosure relates to a method for detecting a scam address of cryptocurrency using a machine learning model, and the method comprises: acquiring information about scam addresses labeled as being used for a scam transaction and information about benign addresses labeled as being used for a normal transaction from a database; acquiring information about a mule address group used for money laundering on the basis of the scam address group; acquiring feature information corresponding to each of the benign addresses and the addresses included in the scam address group or the mule address group on the basis of at least one of the information about the benign addresses, the information about the scam address group, and the information about the mule address group; and generating a machine learning model by machine learning of the feature information corresponding to each of the addresses and label information corresponding to each of the addresses.

Abstract: The present invention relates to a method for processing a knowledge graph in a data processing apparatus, comprising the steps of: (a) creating a knowledge graph including a taxonomy graph for a classification system of information objects and an entity graph for a relationship between specific information object instances; (b) updating the knowledge graph by reflecting the information objects extracted from a database in the knowledge graph; and (c) inferring the relevance of a random information object by using the updated knowledge graph.

Abstract: The present invention relates to a method for collecting data from a multi-domain in a data collection device. The method includes a step A of collecting data from a general web that is accessible through a search engine; a step B of collecting data from a dark web site that is not accessible with a general web browser and is accessible with preset specific software; and a step C of standardizing the collected data in a preset format and generating metadata for the collected data.

Abstract: A method, an apparatus, and a computer program for analyzing performance of a network application program are provided. The method for analyzing performance of a network application program which operates at a controller in a software defined networking (SDN) environment may include receiving identification information of the application program, flow information, and network configuration information of a network controlled by the controller, emulating a virtual network using the network configuration information, executing the virtual network using the flow information, measuring a time, when a virtual controller processes a flow, on a function basis, extracting information corresponding to the identification information of the application program from the measured result, and analyzing a performance degradation factor of the application program using the extracted information.

Abstract: Disclosed are an apparatus, a method, and a computer program by which it is determined whether a target network program generated in a software defined network is malicious by extracting a feature of a behavior graph of the target network program and applying machine learning to the behavior graph. Accordingly, a security and safety of a software defined network may be improved by detecting whether a computer program is malicious before the malware is installed.

Abstract: A method, an apparatus, and a computer program for analyzing performance of a network application program are provided. The method for analyzing performance of a network application program which operates at a controller in a software defined networking (SDN) environment may include receiving identification information of the application program, flow information, and network configuration information of a network controlled by the controller, emulating a virtual network using the network configuration information, executing the virtual network using the flow information, measuring a time, when a virtual controller processes a flow, on a function basis, extracting information corresponding to the identification information of the application program from the measured result, and analyzing a performance degradation factor of the application program using the extracted information.