• Journal of the Korea Convergence Society
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• v.9 no.7
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• pp.55-62
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• 2018

Recently, as the threat of cyber crime increases, the importance of security control to cope with cyber attacks on the information systems in the first place such as real-time detection is increasing. In the name of security control center, cyber terror response center and infringement response center, institutional control personnel are making efforts to prevent cyber attacks. Especially, we are detecting infringement accident by using network security equipment or utilizing control system, but it's not enough to prevent infringement accident by just controlling based on device-driven simple patterns. Therefore, the security control system is continuously being upgraded, and the development and research on the detection method are being actively carried out by the prevention activity against the threat of infringement. In this paper, we have defined the method of detecting infringement of major component module in order to improve the problem of existing infringement detection method. Through the performance tests for each module, we propose measures for effective security control and study effective infringement threat detection method by upgrading the control system using Security Information Event Management (SIEM).

• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.618-628
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• 2020

The purpose of this paper is to propose a new security orchestration service model by combining various security solutions that have been introduced and operated individually as a basis for cyber security framework. At present, in order to respond to various and intelligent cyber attacks, various single security devices and SIEM and AI solutions that integrate and manage them have been built. In addition, a cyber security framework and a security control center were opened for systematic prevention and response. However, due to the document-oriented cybersecurity framework and limited security personnel, the reality is that it is difficult to escape from the control form of fragmentary infringement response of important detection events of TMS / IPS. To improve these problems, based on the model of this paper, select the targets to be protected through work characteristics and vulnerable asset identification, and then collect logs with SIEM. Based on asset information, we established proactive methods and three detection strategies through threat information. AI and SIEM are used to quickly determine whether an attack has occurred, and an automatic blocking function is linked to the firewall and IPS. In addition, through the automatic learning of TMS / IPS detection events through machine learning supervised learning, we improved the efficiency of control work and established a threat hunting work system centered on big data analysis through machine learning unsupervised learning results.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.6
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• pp.807-816
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• 2018

Threat hunting is defined as a process of proactively and iteratively searching through networks to detect and isolate advanced threats that evade existing security solutions. The main concept of threat hunting is to find out weak points and remedy them before actual cyber threat has occurred. And HMM(Hunting Maturity Matrix) is suggested to evolve hunting processes with five levels, therefore, CSOC(Cyber Security Operations Center) can refer HMM how to make them safer from complicated and organized cyber attacks. We are developing a system for cyber situation awareness system with pro-active threat hunting process called unMazeTM. With this unMaze, it can be upgraded CSOC's HMM level from initial level to basic level. CSOC with unMaze do threat hunting process not only detecting existing cyber equipment post-actively, but also proactively detecting cyber threat by fusing and analyzing cyber asset data and threat intelligence.