Journal of Convergence for Information Technology (융합정보논문지)

Convergence Society for SMB (중소기업융합학회)
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Intrusion Situation Classification Model for Intelligent Intrusion Awareness

지능적인 침입 인지를 위한 침입 상황 분류 모델

  • Hwang, Yoon-Cheol (Department of Talmage Liberal Arts College, Hannam University) ;
  • Mun, Hyung-Jin (Department of Information & Communication Engineering, Sungkyul University)
  • 황윤철 (한남대학교 탈메이지 교양교육대학) ;
  • 문형진 (성결대학교 정보통신공학부)
  • Received : 2019.01.11
  • Accepted : 2019.03.20
  • Published : 2019.03.28
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Abstract

As the development of modern society progresses rapidly, the technologies of society as a whole are progressing and becoming more advanced. Especially in the field of security, more sophisticated and intelligent attacks are being created. Meanwhile, damaging situations are becoming several times larger than before Therefore, it is necessary to re-classify and enhance the existing classification system. It is required to minimize the intrusion damage by actively responding to intelligent intrusions by applying this classification scheme to currently operating intrusion detection systems. In this paper, we analyze the intrusion type caused by intelligent attack We propose a new classification scheme for intrusion situations to guarantee the service safety, reliability, and availability of the target system, We use this classification model to lay the foundations for the design and implementation of a smart intrusion cognitive system capable of early detection of intrusion, the damages caused by intrusion, and more collections active response.

현대 사회의 발전이 급속하게 진행됨에 따라 이를 뒷받침 하는 사회 전반의 기술들도 전보다 한층 진보되고 지능화되고 있다. 특히 보안 분야에서도 기존의 공격보다 더 정교하고 지능화된 공격들이 새로 생성되고 있고 그 피해 상황도 전보다 몇 배나 크게 발생되고 있다. 기존의 침입에 대한 분류체계를 현시점에 맞게 재정립하고 분류할 필요가 있고, 현재 작동하고 있는 침입탐지 및 감지 시스템들에 이런 분류체계를 적용하여 지능화된 침입에 능동적으로 대응하여 침입 피해를 최소화하는 것이 요구되고 있다. 본 논문에서는 현재 지능적인 공격에 의해 발생하는 침입 유형을 분석하여, 목적하는 시스템의 서비스 안전성, 신뢰성, 가용성을 보장하기 위한 새로운 침입 상황분류 모델을 제안하고, 이 분류 모델을 사용하여 조기에 침입을 감지하여 침입 피해를 최소화하고 보다 능동적인 대응이 가능한 스마트한 침입 인지 시스템을 설계하고 구현하는 연구에 토대를 마련한다.

Keywords

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Fig 1. Intrusion Situation Awareness Process Model

Table 1. Intrusion Situation Awareness Classification Model

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Table 2. SW Service Network Classification according to proposed classification Model

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Table 3. Cryptojacking Classification according to proposed classification model

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References

  1. R. Von Solms & J. Van Niekerk. (2013). From information security to cyber security. computers & security, 38, 97-102. https://doi.org/10.1016/j.cose.2013.04.004
  2. K. Panetta. (2017). 5 Trends in Cybersecurity for 2017 and 2018. Smarter with Gartner.
  3. Symantec. (2017). 2017 Internet Security Threat Report. 22.https://www.symantec.com/content/dam/symantec/doc s/reports/istr-22-2017-en.pdf
  4. Y. X. Meng. (2011). The practice on using machine learning for network anomaly intrusion detection. In Machine Learning and Cybernetics (ICMLC), 2011 International Conference on, 2(1), 576-581.
  5. H. J. Seo, D. G. Lee, J. S. Choi & H. W. Kim.(2013). IoT Security Technology Trend. Journal of KIEES, 24(4), 27-35.
  6. M. Abomhara. (2015). Cyber Security and the Internet of Things: Vulnerabilities, Threats, Intruders and Attacks. Journal of Cyber Security, 4, 65-88. https://doi.org/10.13052/jcsm2245-1439.414
  7. H. H. Lee, Y. Y. Lee & J. S. An. (2016). Commercial and Public Software Intentional Security Weakness Trend. Journal of Information Security, 26(1), 9-19.
  8. W. S. Choi, H. S. Kim & D. H. Lee. (2018). Cryptojacking Research Trends. Journal of Information Security, 28(3), 33-37.
  9. H. J. Mun. (2018). Biometric Information and OTP based on Authentication Mechanism using Blockchain. Journal of Convergence for Information Technology, 8(3), 85-90. https://doi.org/10.22156/CS4SMB.2018.8.3.085
  10. H. J. Mun, Y. C. Hwang & H. Y. Kim. (2015). Countermeasure for Prevention and Detection against Attacks to SMB Information System - A Survey. Journal of Convergence for Information Technology, 5(2), 1-6. https://doi.org/10.22156/CS4SMB.2015.5.2.001
  11. K. S. Kim & M. S. Kang. (2014). Next Generation Cyber Security Issues, Threats and Countermeasures. Journal of Electrical Engineering, 41(4), 69-77.
  12. AhnLab. (2018). security threat trend.
  13. C. T. Lim, J. H. Oh & H. C. Jung. (2010). Trend of Malicious Code Technology and Analysis Method. Information Science Society, 28(11), 117-126.
  14. M. S Gu & Y. Z. Lee. (2015). A Study of Countermeasures for Advanced Persistent Threats attacks by malicious code. Journal of Convergence for Information Technology, 5(4), 37-42. https://doi.org/10.14801/jaitc.2015.5.2.37
  15. H. J. Mun, S. H. Choi & Y. C. Hwang. (2016). Effective Countermeasure to APT Attacks using Big Data. Journal of Convergence for Information Technology, 6(1), 17-23. https://doi.org/10.5121/ijitcs.2016.6402