DATA-DRIVEN FIELD MAPPING OF SECURITY LOGS FOR INTEGRATED MONITORING

被引:0
作者
Choi, Seungoh [1 ]
Kim, Yesol [1 ]
Yun, Jeong-Han [1 ]
Min, Byung-Gil [1 ]
Kim, Hyoung-Chun [1 ]
机构
[1] Affiliated Inst ETRI, Daejeon, South Korea
来源
CRITICAL INFRASTRUCTURE PROTECTION XIII | 2019年 / 570卷
关键词
Security; event logs; integrated system monitoring;
D O I
10.1007/978-3-030-34647-8_13
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
As industrial control system vulnerabilities and attacks increase, security controls must be applied to operational technologies. The growing demand for security threat monitoring and analysis techniques that integrate information from security logs has resulted in enterprise security management systems giving way to security information and event management systems. Nevertheless, it is vital to implement some form of pre-processing to collect, integrate and analyze security events efficiently. Operators still have to manually check entire security logs or write scripts or parsers that draw on domain knowledge, tasks that are time-consuming and error-prone. To address these challenges, this chapter focuses on the data-driven mapping of security logs to support the integrated monitoring of operational technology systems. The characteristics of security logs from security appliances used in critical infrastructure assets are analyzed to create a tool that maps different security logs to field categories to support integrated system monitoring. The tool reduces the effort needed by operators to manually process security logs even when the logged data generated by security appliances has new or modified formats.
引用
收藏
页码:253 / 268
页数:16
相关论文
共 50 条
  • [31] A Hybrid Data-driven Model for Intrusion Detection in VANET
    Bangui, Hind
    Ge, Mouzhi
    Buhnova, Barbora
    12TH INTERNATIONAL CONFERENCE ON AMBIENT SYSTEMS, NETWORKS AND TECHNOLOGIES (ANT) / THE 4TH INTERNATIONAL CONFERENCE ON EMERGING DATA AND INDUSTRY 4.0 (EDI40) / AFFILIATED WORKSHOPS, 2021, 184 : 516 - 523
  • [32] A Data-driven Attack against Support Vectors of SVM
    Liu, Shigang
    Zhang, Jun
    Wang, Yu
    Zhou, Wanlei
    Xiang, Yang
    De Vel, Olivier
    PROCEEDINGS OF THE 2018 ACM ASIA CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY (ASIACCS'18), 2018, : 723 - 734
  • [33] DiffGen: a data-driven framework for generating truncated differentials
    Idris, Mohamed Fadl
    Teh, Je Sen
    Yusoff, Mohd Najwadi
    APPLIED INTELLIGENCE, 2025, 55 (05)
  • [34] Data-Driven Screening of Network Constraints for Unit Commitment
    Pineda, Salvador
    Morales, Juan Miguel
    Jimenez-Cordero, Asuncion
    IEEE TRANSACTIONS ON POWER SYSTEMS, 2020, 35 (05) : 3695 - 3705
  • [35] Genetic Informaiton Privacy in the Age of Data-Driven Medicine
    Li, Jingquan
    2016 IEEE INTERNATIONAL CONGRESS ON BIG DATA - BIGDATA CONGRESS 2016, 2016, : 299 - 306
  • [36] Data-driven failure analysis for the cyber physical infrastructures
    Belenko, Viacheslav
    Chernenko, Valery
    Krundyshev, Vasiliy
    Kalinin, Maxim
    2019 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL CYBER PHYSICAL SYSTEMS (ICPS 2019), 2019, : 775 - 779
  • [37] Data-driven Insights from Vulnerability Discovery Metrics
    Munaiah, Nuthan
    Meneely, Andrew
    2019 IEEE/ACM JOINT 4TH INTERNATIONAL WORKSHOP ON RAPID CONTINUOUS SOFTWARE ENGINEERING AND 1ST INTERNATIONAL WORKSHOP ON DATA-DRIVEN DECISIONS, EXPERIMENTATION AND EVOLUTION (RCOSE-DDREE 2019), 2019, : 1 - 7
  • [38] SmartData: Toward the Data-Driven Design of Critical Systems
    Hoffmann, Jose L. Conradi
    Frohlich, Antonio A.
    IEEE ACCESS, 2025, 13 : 41865 - 41886
  • [39] Mapping the field of software life cycle security metrics
    Morrison, Patrick
    Moye, David
    Pandita, Rahul
    Williams, Laurie
    INFORMATION AND SOFTWARE TECHNOLOGY, 2018, 102 : 146 - 159
  • [40] Event-Triggered Data-Driven Security Formation Control for Quadrotors Under Denial-of-Service Attacks and Communication Faults
    Ren, Ziming
    Liu, Hao
    Wen, Guanghui
    Lu, Jinhu
    IEEE TRANSACTIONS ON CYBERNETICS, 2024,