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公开(公告)号:US20190327164A1
公开(公告)日:2019-10-24
申请号:US16206662
申请日:2018-11-30
Inventor: Jung Tae KIM , Youngsoo KIM , Jonghyun KIM , Hyun Joo KIM , Jong Geun PARK , Sang-Min LEE , Jong-Hoon LEE , Sunoh CHOI
IPC: H04L12/26
Abstract: A method, an apparatus, and a system for analyzing traffic through obtaining flow data of a flow from a switch or a router of a network, calculating an average byte per packet rate (BPR) and a TCP flag ratio (TCPFR) for all flows included in a session including the flow using the flow data, and comparing the average BPR and the TCPFR with an average BPR and a TCPFR of previously-known traffic and determining whether the traffic including the flow is normal traffic or abnormal traffic based on the comparison result are provided.
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公开(公告)号:US20140295787A1
公开(公告)日:2014-10-02
申请号:US14094897
申请日:2013-12-03
Inventor: Keonwoo KIM , Sung Kyong UN , Youn-Hee GIL , Youngsoo KIM , Sang Su LEE , Jooyoung LEE , Su Hyung JO , Woo Yong CHOI
IPC: H04M15/06
Abstract: A method for visualizing record data with a mobile forensic device for collecting and managing the record data including partner information when an event, such as a call or text service, occurs, includes: calculating an event occurrence frequency for each partner information using the collected record data; and classifying the partner information on the basis of a grade according to the calculated event occurrence frequency. Further, the method includes generating graphic data showing a relation between the partner information and user information of a mobile terminal on the basis of the classified grade; and displaying the graphic data.
Abstract translation: 一种用于通过移动取证设备可视化记录数据的方法,用于在发生诸如呼叫或文本服务的事件时收集和管理包括伙伴信息的记录数据,包括:使用所收集的记录来计算每个伙伴信息的事件发生频率 数据; 并根据所计算的事件发生频率,根据成绩对合作伙伴信息进行分类。 此外,该方法包括基于分级等级生成表示移动终端的伙伴信息和用户信息之间的关系的图形数据; 并显示图形数据。
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公开(公告)号:US20190394215A1
公开(公告)日:2019-12-26
申请号:US16202869
申请日:2018-11-28
Inventor: Jong-Hoon LEE , Youngsoo KIM , Ik Kyun KIM , Jung Tae KIM , Jonghyun KIM , Hyun Joo KIM , Jong Geun PARK , Sang-Min LEE , Sunoh CHOI
Abstract: A method and a computation apparatus detecting cyber threats using a neural network through steps of: generating a learning model by performing machine learning on training data based on baseline data, converting a security event collected in real time into input data for the neural network, and determining, as an output corresponding to the input data based on the learning model, whether the security event is normal or threat are provided.
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