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1.发明申请公开(公告)号：US20130318609A1
公开(公告)日：2013-11-28
申请号：US13902069
申请日：2013-05-24
Inventor： Ki Young KIM , Sungwon YI , Sun Hee LIM , Jonghyun KIM , Dae-Hee SEO , BYUNG-GIL LEE
IPC: H04L29/06
CPC classification number: H04L63/1441 , H04L63/1425
Abstract: An apparatus for quantifying network threat situations includes a traffic analyzing unit to analyze packet patterns of traffics occurring on a target network being monitored to extract one or more suspicious domains. An IP monitoring unit gives security levels among a plurality of security levels to the suspicious domains according to the number of access IPs accessing the suspicious domains. An activity index computing unit computes activity indices for the suspicious domains from activity indices according to the access times to the suspicious domains of the access IPs. An attack amount anticipation unit analogizes an expected amount of attacks for each suspicious domain according to an expected amount of attacks for each zombie computer, the security level and the activity index of the suspicious domain.
Abstract translation: 用于量化网络威胁情况的装置包括业务分析单元，用于分析在被监视的目标网络上发生的流量的分组模式，以提取一个或多个可疑域。 IP监控单元根据访问可疑域的访问IP的数量，向可疑域提供多个安全级别之间的安全级别。 活动索引计算单元根据访问IP的可疑域的访问时间，从活动索引计算可疑域的活动索引。 攻击量预期单元根据每个僵尸计算机的预期攻击次数，可疑域的安全级别和活动索引类似于每个可疑域的预期攻击次数。
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Patent Agency Ranking
2.发明公开公开(公告)号：US20240256885A1
公开(公告)日：2024-08-01
申请号：US18517931
申请日：2023-11-22
Inventor： Byunghyun YOO , Hyun Woo KIM , Hwajeon SONG , Jeongmin YANG , Sungwon YI , Euisok CHUNG , Ran HAN
IPC: G06N3/092
CPC classification number: G06N3/092
Abstract: Provided is an exploration method based on reward decomposition in multi-agent reinforcement learning. The exploration method includes: generating a positive reward estimation model through neural network training based on training data including states of all agents, actions of all the agents, and a global reward true value; generating, for each of the agents, a first individual utility function based on the global reward true value and generating a second individual utility function using the positive reward estimation model; and determining an action of each of the agents using the first individual utility function and the second individual utility function based on the state of each of the agents.
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3.发明申请公开(公告)号：US20160295570A1
公开(公告)日：2016-10-06
申请号：US15089361
申请日：2016-04-01
Inventor： Kwi Hoon KIM , Young Boo KIM , Hyun Jae KIM , Jin Tae OH , Woong Shik YOU , Sungwon YI
CPC classification number: H04L5/005 , H04B7/0413 , H04L5/0048 , H04L5/0064
Abstract: Provided herein is a communication method using MIMO (Multiple-Input Multiple-Output) technology for communicating with a terminal included in each of a plurality of base station cells using a communication apparatus, the method including calculating, by the communication apparatus, the number of terminals included inside a base station cell; generating, by the communication apparatus, pilot signals corresponding to the calculated number of terminals; and allocating, by the communication apparatus, the pilot signals to a terminal that may maximize a network capacity based on the generated pilot signals.
Abstract translation: 本文提供了一种使用MIMO（多输入多输出）技术的通信方法，用于使用通信装置与包括在多个基站小区中的每一个中的终端通信，该方法包括由通信装置计算 包括在基站小区内的终端; 由通信装置产生与计算出的终端数相对应的导频信号; 以及由所述通信装置将导频信号分配给可以基于所生成的导频信号来最大化网络容量的终端。
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公开(公告)号：US20230186154A1
公开(公告)日：2023-06-15
申请号：US17893628
申请日：2022-08-23
Inventor： Byunghyun YOO , Hyun Woo KIM , Jeon Gue PARK , Hwa Jeon SONG , Jeongmin YANG , Sungwon YI , Euisok CHUNG , Ran HAN
IPC: G06N20/00
CPC classification number: G06N20/00
Abstract: An exploration method used by an exploration apparatus in multi-agent reinforcement learning to collect training samples during the training process is provided. The exploration method includes calculating the influence of a selected action of each agent on the actions of other agents in a current state, calculating a linear sum of the value of a utility function representing the action value of each agent and the influence on the actions of the other agent calculated for the selected action of each agent, and obtaining a sample to be used for training an action policy of each agent by probabilistically selecting the action in which the linear sum is the maximum, and the random action.
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公开(公告)号：US20220215204A1
公开(公告)日：2022-07-07
申请号：US17570226
申请日：2022-01-06
Inventor： Ningombam Devarani Devi , Sungwon YI , Hyun Woo KIM , Hwa Jeon SONG , Byung Hyun YOO
IPC: G06K9/62
Abstract: Provided is a method for exploration based on curiosity and prioritization of experience data in multi-agent reinforcement learning, the method including the steps of: calculating a similarity between a policy of a first agent and a policy of a second agent and computing a final reward using the similarity; and performing clustering on a replay buffer using a result of calculating the similarity between the policy of the first agent and the policy of the second agent and performing sampling on data in the cluster.
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6.发明申请公开(公告)号：US20170142136A1
公开(公告)日：2017-05-18
申请号：US15225560
申请日：2016-08-01
Inventor： Sungwon YI
CPC classification number: H04L63/1416 , G06F17/30312
Abstract: There are provided a method and an apparatus for detecting attacks and automatically generating attack signatures based on signature merging. A method for detecting attacks and automatically generating attack signatures based on signature merging includes detecting a character string matched to at least one previously stored compressed attack signature in an input packet received from a network, determining whether the character string detected in the primary attack detection is matched to at least one previously stored individual attack signature, and, if the detected character string is matched to the at least one previously stored individual attack signature, determining the input packet as an attack packet, and, if the detected character string is not matched, determining the input packet as a new attack signature.
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