DRACE: A Framework for Evaluating Anomaly Detectors for Industrial Control Systems

被引：0
|
作者：
Christian, Ivan [1 ]
Furtado, Francisco [1 ]
Mathur, Aditya P. [1 ]
机构：
[1] SUTD, ITrust, Singapore, Singapore
来源：
PROCEEDINGS OF THE 10TH ACM CYBER-PHYSICAL SYSTEM SECURITY WORKSHOP, ACM CPSS 2024 | 2024年
基金：
新加坡国家研究基金会;
关键词：
Anomaly detection; Cyber exercise; Industrial Control Systems; Cyber Security; Performance Metrics; Critical Infrastructure; Tools;
D O I：
10.1145/3626205.3659145
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The detection of process anomalies is a critical step in defending a physical plant against cyber-attacks. We propose a framework named DRACE that includes a set of metrics to evaluate the effectiveness of anomaly detectors, referred to as Intrusion Detection Systems (IDS). Different from those used in the literature, the proposed metrics are designed to serve as a means for plant engineers and IT specialists to compare multiple detectors prior to deciding which to deploy. The metrics were found effective in evaluating the effectiveness of several anomaly detectors of different origins in a case study conducted in the iTrust laboratory.
引用
收藏
页码：77 / 87
页数：11
相关论文
共 50 条
  • [31] Anomaly detection for industrial control systems using process mining
    Myers, David
    Suriadi, Suriadi
    Radke, Kenneth
    Foo, Ernest
    COMPUTERS & SECURITY, 2018, 78 : 103 - 125
  • [32] Machine Learning Methods for Anomaly Detection in Industrial Control Systems
    Tai, Johnathan
    Alsmadi, Izzat
    Zhang, Yunpeng
    Qiao, Fengxiang
    2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2020, : 2333 - 2339
  • [33] Research on Improvement of Anomaly Detection Performance in Industrial Control Systems
    Bae, Sungho
    Hwang, Chanwoong
    Lee, Taejin
    INFORMATION SECURITY APPLICATIONS, 2021, 13009 : 76 - 87
  • [34] State-Aware Anomaly Detection for Industrial Control Systems
    Ghaeini, Hamid Reza
    Antonioli, Daniele
    Brasser, Ferdinand
    Sadeghi, Ahmad-Reza
    Tippenhauer, Nils Ole
    33RD ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING, 2018, : 1620 - 1628
  • [35] An Anomaly Detection Technique for Deception Attacks in Industrial Control Systems
    Qassim, Q. S.
    Ahmad, A. R.
    Ismail, R.
    Bakar, Abu A.
    Rahim, Abdul F.
    Mokhtar, M. Z.
    Ramli, R.
    Mohd, Yusof B.
    Mahdi, Mohammed Najah
    2019 IEEE 5TH INTL CONFERENCE ON BIG DATA SECURITY ON CLOUD (BIGDATASECURITY) / IEEE INTL CONFERENCE ON HIGH PERFORMANCE AND SMART COMPUTING (HPSC) / IEEE INTL CONFERENCE ON INTELLIGENT DATA AND SECURITY (IDS), 2019, : 267 - 272
  • [36] Anomaly detection using invariant rules in Industrial Control Systems
    Zhu, Qilin
    Ding, Yulong
    Jiang, Jie
    Yang, Shuang-Hua
    CONTROL ENGINEERING PRACTICE, 2025, 154
  • [37] Adaptable and Interpretable Framework for Anomaly Detection in SCADA-based industrial systems
    Wadinger, Marek
    Kvasnica, Michal
    EXPERT SYSTEMS WITH APPLICATIONS, 2024, 246
  • [38] A Novel Data Collection Framework for Telemetry and Anomaly Detection in Industrial IoT Systems
    De Vita, Fabrizio
    Bruneo, Dario
    Das, Sajal K.
    2020 ACM/IEEE FIFTH INTERNATIONAL CONFERENCE ON INTERNET OF THINGS DESIGN AND IMPLEMENTATION (IOTDI 2020), 2020, : 245 - 251
  • [39] Using Systems Engineering to Create a Framework for Evaluating Industrial Symbiosis Options
    Sopha, Bertha Maya
    Fet, Annik Magerholm
    Keitsch, Martina Maria
    Haskins, Cecilia
    SYSTEMS ENGINEERING, 2010, 13 (02) : 149 - 160
  • [40] Probing the Limits of Anomaly Detectors for Automobiles with a Cyberattack Framework
    Taylor, Adrian
    Leblanc, Sylvain
    Japkowicz, Nathalie
    IEEE INTELLIGENT SYSTEMS, 2018, 33 (02) : 54 - 62
12345