보안 위험 평가를 위한 사회공학 공격 그래프 : Social Engineering Attack Graph framework(SEAG)
Social Engineering Attack Graph for Security Risk Assessment: Social Engineering Attack Graph framework(SEAG)
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
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.
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.
참고문헌 (20)
- Mitnick, Kevin D., "The art of deception: Controlling the human element of security" (2011), John Wiley & Sons
- Hadnagy, Christopher, "Social engineering: The art of human hacking" (2010), John Wiley & Sons
- Artz, Michael Lyle, "Netspa: A network security planning architecture", pp.2002
- Ou, Xinming, "MulVAL: A Logic-based Network Security Analyzer", USENIX Security Symposium, 8 (2005)
- Ou, Xinming, "A scalable approach to attack graph generation", Proceedings of the 13th ACM conference on Computer and communications security. ACM (2006)
- Ingols, Kyle, "Practical attack graph generation for network defense", Computer Security Applications Conference, 2006. ACSAC'06. 22nd Annual. IEEE (2006)
- Poolsappasit, Nayot, "Dynamic security risk management using bayesian attack graphs", IEEE Transactions on Dependable and Secure Computing, 9(1), pp.61~74 (2012)
- Wang, Lingyu, "An attack graph-based probabilistic security metric", IFIP Annual Conference on Data and Applications Security and Privacy. Springer (2008)
- Keramati, Marjan, "CVSS-based security metrics for quantitative analysis of attack graphs", Computer and Knowledge Engineering (ICCKE), 2013 3th International eConference on. IEEE (2013)
- Wang, Lingyu, "k-zero day safety: A network security metric for measuring the risk of unknown vulnerabilities", IEEE Transactions on Dependable and Secure Computing, 11(1), pp.30~44 (2014)
- Yusuf, Simon Enoch, "Security Modelling and Analysis of Dynamic Enterprise Networks", Computer and Information Technology (CIT), 2016 IEEE International Conference on. IEEE (2016)
- Moon, Young Hoon, "Hybrid Attack Path Enumeration System Based on Reputation Scores", Computer and Information Technology (CIT), 2016IEEE International Conference on. IEEE (2016)
- Ge, Mengmeng, "Evaluating Security and Availability of Multiple Redundancy Designs when Applying Security Patches", Dependable Systems and Networks Workshop (DSN-W), 2017 47th Annual IEEE, 2017, ~
- Dimkov, Trajce, "Two methodologies for physical penetration testing using social engineering", Proceedings of the 26th annual computer security applications conference. ACM (2010)
- Ivaturi, Koteswara, "A taxonomy for social engineering attacks", International Conference on Information Resources Management. Centre for Information Technology, Organizations, and People (2011)
- Pavković, Nikola, "Social Engineering Toolkit—A systematic approach to social engineering", MIPRO, 2011 Proceedings of the 34th International Convention. IEEE (2011)
- Algarni, Abdullah, "Social engineering in social networking sites: Affect-based model", Internet technology and secured transactions (icitst), 2013 8th international conference for. IEEE (2013)
- Mouton, Francois, "Social engineering attack framework", Information Security for South Africa (ISSA), 2014. IEEE (2014)
- Beckers, Kristian, "Data privacy management, autonomous spontaneous security, and security assurance", pp.216~232 (2015), Springer
- 문주연, "동적 네트워크 환경에 적용 가능한 Attack Graph 모델 연구", 정보보호학회논문지, 28(2), pp.485~500 (2018), 한국정보보호학회[DOI]
