公开(公告)号：US20190213505A1

公开(公告)日：2019-07-11

申请号：US16170779

申请日：2018-10-25

Applicant: AGENCY FOR DEFENSE DEVELOPMENT

Inventor： Changhee CHOI ,  Hwaseong LEE ,  Ilhoon JUNG ,  Changon YOO ,  Hosang YUN

IPC: G06N99/00 ,  G06F17/30

Abstract: There is provided an email authorship classification apparatus. The apparatus includes an information analysis unit configured to analyze header field information in an attribute header of each of emails and extract feature field information related to an authorship of each of the emails from each of the header field information and an information conversion unit configured to convert the feature field information into a feature data set for inputting a learning model thereto. The apparatus further includes a learning model unit configured to generate a classification model for classifying the emails by author by applying a learning process to the feature data set.

公开(公告)号：US20230057138A1

公开(公告)日：2023-02-23

申请号：US17456529

申请日：2021-11-24

Applicant: AGENCY FOR DEFENSE DEVELOPMENT

Inventor： Hwaseong LEE ,  Jeongchan PARK ,  Changon YOO ,  Jingoog KIM ,  Yeojeong YOON ,  Ilhoon JUNG ,  Sung Jin PARK

IPC: G06F21/71 ,  G06N20/20

Abstract: This application relates to an apparatus for identifying an abnormal processor. In one aspect, the apparatus includes a memory information collection part that collects memory region information and dynamic library information of a memory. The apparatus may also include a first identification part that identifies an abnormal processor by using a first machine learning model pre-trained based on the memory region information. The apparatus may further include a second identification part that identifies an abnormal processor by using a second machine learning model pre-trained based on the dynamic library information. The apparatus may further include a determination part that determines a final abnormal processor based on either the abnormal processor identified using the first machine learning model or the abnormal processor identified using the second machine learning model.