• Title/Summary/Keyword: SOC(Security Operation Center)

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Trends of SOC & SIEM Technology for Cybersecurity (Cybersecurity를 위한 SOC & SIEM 기술의 동향)

  • Cha, ByungRae;Choi, MyeongSoo;Kang, EunJu;Park, Sun;Kim, JongWon
    • Smart Media Journal
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    • v.6 no.4
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    • pp.41-49
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    • 2017
  • According to the occurrence of many security incidents, the SOC(Security Operation Center) and SIEM(Security Information & Event Management) are concentrated recently. The various studies and commercial products of the information security industry are being released. As reflected in this situation, NIST in the US is publishing and revising the document about the Cybersecurity Framework. In this study, we investigated the NIST's Cyberseurity Framework, trends in SOC and SIEM security technologies and solutions, and also introduce the open source Apache Metron of a real-time Bigdata security tool.

A Real-Time and Statistical Visualization Methodology of Cyber Threats Based on IP Addresses (IP 주소 기반 사이버공격 실시간 및 통계적 가시화 방법)

  • Moon, Hyeongwoo;Kwon, Taewoong;Lee, Jun;Ryou, Jaecheol;Song, Jungsuk
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.30 no.3
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    • pp.465-479
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    • 2020
  • Regardless of the domestic and foreign governments/companies, SOC (Security Operation Center) has operated 24 hours a day for the entire year to ensure the security for their IT infrastructures. However, almost all SOCs have a critical limitation by nature, caused from heavily depending on the manual analysis of human agents with the text-based monitoring architecture. Even though, in order to overcome the drawback, technologies for a comprehensive visualization against complex cyber threats have been studying, most of them are inappropriate for the security monitoring in large-scale networks. In this paper, to solve the problem, we propose a novel visual approach for intuitive threats monitoring b detecting suspicious IP address, which is an ultimate challenge in cyber security monitoring. The approach particularly makes it possible to detect, trace and analysis of suspicious IPs statistically in real-time manner. As a result, the system implemented by the proposed method is suitably applied and utilized to the real-would environment. Moreover, the usability of the approach is verified by successful detecting and analyzing various attack IPs.