From Tactics to Techniques: A Systematic Attack Modeling for Advanced Persistent Threats in Industrial Control Systems

被引:0
|
作者
Yang, Yunhe [1 ]
Zhang, Mu [1 ]
机构
[1] Univ Utah, Salt Lake City, UT 84112 USA
关键词
D O I
10.1109/EuroSPW59978.2023.00042
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Advanced Persistent Threats (APTs) targeting Industrial Control Systems (ICS) have emerged as a significant challenge in the cybersecurity landscape. These sophisticated attacks can lead to catastrophic consequences on critical infrastructure and services. This paper presents an innovative attack model for ICS-APT attacks designed to provide adequate defense against real-world threats. By examining and analyzing real-world APT attacks against ICS, we identify common and unique characteristics across different attacks, bridging the gap between high-level features and low-level behaviors. We further demonstrate the effectiveness of our proposed model by simulating a false data injection attack on a realistic ICS testbed, utilizing the identified Tactics, Techniques, and Procedures (TTPs) and stages of an APT attack. This simulation enables us to validate the model's accuracy and identify potential challenges in mitigating such complex threats. Our proposed model leverages this systematic understanding of attacker behavior, allowing for accurate characterization of attack patterns. It empowers analysts with the tools and insights needed to counteract and mitigate the risk posed by ICS-APT attacks, contributing to the protection of critical infrastructure and enhancing cybersecurity resilience in the face of evolving threats.
引用
收藏
页码:336 / 344
页数:9
相关论文
共 50 条
  • [1] Modeling Observability in Adaptive Systems to Defend Against Advanced Persistent Threats
    Kinneer, Cody
    Wagner, Ryan
    Fang, Fei
    Le Goues, Claire
    Garlan, David
    17TH ACM-IEEE INTERNATIONAL CONFERENCE ON FORMAL METHODS AND MODELS FOR SYSTEM DESIGN (MEMOCODE), 2019,
  • [2] Systems Dynamics Modeling for Evaluating SocioTechnical Vulnerabilities in Advanced Persistent Threats
    Nicho, Mathew
    Girija, Shini
    2022 15TH INTERNATIONAL CONFERENCE ON HUMAN SYSTEM INTERACTION (HSI), 2022,
  • [3] A performance overview of machine learning-based defense strategies for advanced persistent threats in industrial control systems
    Imran, Muhammad
    Siddiqui, Hafeez Ur Rehman
    Raza, Ali
    Raza, Muhammad Amjad
    Rustam, Furqan
    Ashraf, Imran
    COMPUTERS & SECURITY, 2023, 134
  • [4] Modeling social worm propagation for advanced persistent threats
    Zhou, Peng
    Gu, Xiaojing
    Nepal, Surya
    Zhou, Jianying
    COMPUTERS & SECURITY, 2021, 108
  • [5] Modeling Attack Process of Advanced Persistent Threat
    Niu, Weina
    Zhan, Xiaosong
    Li, Kenli
    Yang, Guowu
    Chen, Ruidong
    SECURITY, PRIVACY, AND ANONYMITY IN COMPUTATION, COMMUNICATION, AND STORAGE, 2016, 10066 : 383 - 391
  • [6] Preventing Advanced Persistent Threats in Complex Control Networks
    Rubio, Juan E.
    Alcaraz, Cristina
    Lopez, Javier
    COMPUTER SECURITY - ESORICS 2017, PT II, 2017, 10493 : 402 - 418
  • [7] A systematic literature review on past attack analysis on industrial control systems
    Goel, Swati
    TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES, 2024, 35 (06):
  • [8] Modeling advanced persistent threats using risk matrix methods
    Nina D. Ivanova
    Vitaliy G. Ivanenko
    Journal of Computer Virology and Hacking Techniques, 2023, 19 : 367 - 372
  • [9] Modeling advanced persistent threats using risk matrix methods
    Ivanova, Nina D.
    Ivanenko, Vitaliy G.
    JOURNAL OF COMPUTER VIROLOGY AND HACKING TECHNIQUES, 2023, 19 (03) : 367 - 372
  • [10] Advanced Persistent Threats and Their Defense Methods in Industrial Internet of Things: A Survey
    Gan, Chenquan
    Lin, Jiabin
    Huang, Da-Wen
    Zhu, Qingyi
    Tian, Liang
    MATHEMATICS, 2023, 11 (14)