보안 위험 평가를 위한 사회공학 공격 그래프 : Social Engineering Attack Graph framework(SEAG)
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김준석 | - |
dc.contributor.author | 강현재 | - |
dc.contributor.author | 김진수 | - |
dc.contributor.author | 김휘강 | - |
dc.date.accessioned | 2021-09-02T19:03:45Z | - |
dc.date.available | 2021-09-02T19:03:45Z | - |
dc.date.created | 2021-06-17 | - |
dc.date.issued | 2018 | - |
dc.identifier.issn | 1598-849X | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/79635 | - |
dc.description.abstract | Social engineering attack means to get information of Social engineering attack means to get information of opponent without technical attack or to induce opponent to provide information directly. In particular, social engineering does not approach opponents through technical attacks, so it is difficult to prevent all attacks with high-tech security equipment. Each company plans employee education and social training as a countermeasure to prevent social engineering. However, it is difficult for a security officer to obtain a practical education(training) effect, and it is also difficult to measure it visually. Therefore, to measure the social engineering threat, we use the results of social engineering training result to calculate the risk by system asset and propose a attack graph based probability. The security officer uses the results of social engineering training to analyze the security threats by asset and suggests a framework for quick security response. Through the framework presented in this paper, we measure the qualitative social engineering threats, collect system asset information, and calculate the asset risk to generate probability based attack graphs. As a result, the security officer can graphically monitor the degree of vulnerability of the asset's authority system, asset information and preferences along with social engineering training results. It aims to make it practical for companies to utilize as a key indicator for establishing a systematic security strategy in the enterprise. | - |
dc.language | Korean | - |
dc.language.iso | ko | - |
dc.publisher | 한국컴퓨터정보학회 | - |
dc.title | 보안 위험 평가를 위한 사회공학 공격 그래프 : Social Engineering Attack Graph framework(SEAG) | - |
dc.title.alternative | Social Engineering Attack Graph for Security Risk Assessment: Social Engineering Attack Graph framework(SEAG) | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | 김휘강 | - |
dc.identifier.doi | 10.9708/jksci.2018.23.11.075 | - |
dc.identifier.bibliographicCitation | 한국컴퓨터정보학회논문지, v.23, no.11, pp.75 - 84 | - |
dc.relation.isPartOf | 한국컴퓨터정보학회논문지 | - |
dc.citation.title | 한국컴퓨터정보학회논문지 | - |
dc.citation.volume | 23 | - |
dc.citation.number | 11 | - |
dc.citation.startPage | 75 | - |
dc.citation.endPage | 84 | - |
dc.type.rims | ART | - |
dc.identifier.kciid | ART002406755 | - |
dc.description.journalClass | 2 | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | Attack graph | - |
dc.subject.keywordAuthor | Social engineering | - |
dc.subject.keywordAuthor | Risk assessment | - |
dc.subject.keywordAuthor | Network security | - |
dc.subject.keywordAuthor | APT attack | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.