A Spiking One-Class Anomaly Detection Framework for Cyber-Security on Industrial Control Systems

Cyber-Security Controls for Industrial Control Systems

JAPAN TAPPI JOURNAL ◽

10.2524/jtappij.68.275 ◽

2014 ◽

Vol 68 (3) ◽

pp. 275-280

Author(s):

Kentaro Hayashi

Keyword(s):

Control Systems ◽

Cyber Security ◽

Industrial Control ◽

Security Controls

Machine Learning Methods for Anomaly Detection in Industrial Control Systems

2020 IEEE International Conference on Big Data (Big Data) ◽

10.1109/bigdata50022.2020.9378018 ◽

2020 ◽

Author(s):

Johnathan Tai ◽

Izzat Alsmadi ◽

Yunpeng Zhang ◽

Fengxiang Qiao

Keyword(s):

Machine Learning ◽

Anomaly Detection ◽

Control Systems ◽

Industrial Control ◽

Learning Methods ◽

Machine Learning Methods

Challenges in Machine Learning based approaches for Real-Time Anomaly Detection in Industrial Control Systems

Proceedings of the 6th ACM on Cyber-Physical System Security Workshop ◽

10.1145/3384941.3409588 ◽

2020 ◽

Cited By ~ 1

Author(s):

Chuadhry Mujeeb Ahmed ◽

Gauthama Raman M R ◽

Aditya P. Mathur

Keyword(s):

Machine Learning ◽

Anomaly Detection ◽

Control Systems ◽

Real Time ◽

Industrial Control ◽

Detecting cyberattacks using anomaly detection in industrial control systems: A Federated Learning approach

Computers in Industry ◽

10.1016/j.compind.2021.103509 ◽

2021 ◽

Vol 132 ◽

pp. 103509

Author(s):

Truong Thu Huong ◽

Ta Phuong Bac ◽

Dao Minh Long ◽

Tran Duc Luong ◽

Nguyen Minh Dan ◽

...

Keyword(s):

Anomaly Detection ◽

Control Systems ◽

Learning Approach ◽

Industrial Control ◽

DAICS: A Deep Learning Solution for Anomaly Detection in Industrial Control Systems

IEEE Transactions on Emerging Topics in Computing ◽

10.1109/tetc.2021.3073017 ◽

2021 ◽

pp. 1-1

Author(s):

Maged Fathy Abdelaty ◽

Roberto Doriguzzi Corin ◽

Domenico Siracusa

Keyword(s):

Deep Learning ◽

Anomaly Detection ◽

Control Systems ◽

Industrial Control ◽

A Model-Data Integrated Cyber Security Risk Assessment Method for Industrial Control Systems

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) ◽

10.1109/ddcls.2018.8516022 ◽

2018 ◽

Author(s):

Yuan Peng ◽

Kaixing Huang ◽

Weixun Tu ◽

Chunjie Zhou

Keyword(s):

Risk Assessment ◽

Control Systems ◽

Cyber Security ◽

Assessment Method ◽

Security Risk ◽

Model Data ◽

Industrial Control ◽

Risk Assessment Method ◽

Security Risk Assessment

AADS: A Noise-Robust Anomaly Detection Framework for Industrial Control Systems

Information and Communications Security - Lecture Notes in Computer Science ◽

10.1007/978-3-030-41579-2_4 ◽

2020 ◽

pp. 53-70

Author(s):

Maged Abdelaty ◽

Roberto Doriguzzi-Corin ◽

Domenico Siracusa

Keyword(s):

Anomaly Detection ◽

Control Systems ◽

Industrial Control ◽

Noise Robust

Comments on Cyber Security in Industrial Control Systems and Automation

Instrument Engineers' Handbook, Volume 3 ◽

10.1201/9781315217093-42 ◽

2018 ◽

pp. 631-637

Keyword(s):

Control Systems ◽

Cyber Security ◽

Industrial Control ◽

Critical Information Infrastructures Security - Lecture Notes in Computer Science ◽

Super Detector: An Ensemble Approach for Anomaly Detection in Industrial Control Systems

10.1007/978-3-030-93200-8_2 ◽

2021 ◽

pp. 24-43

Author(s):

Madhumitha Balaji ◽

Siddhant Shrivastava ◽

Sridhar Adepu ◽

Aditya Mathur

Keyword(s):

Anomaly Detection ◽

Control Systems ◽

Industrial Control ◽

Ensemble Approach

Exploring the Grey Area of Cyber Security in Industrial Control Systems (ICS)

Inside Hazardous Technological Systems ◽

10.1201/9780429281587-14 ◽

2021 ◽

pp. 221-236

Author(s):

Alberto Zanutto

Keyword(s):

Control Systems ◽

Cyber Security ◽

Industrial Control ◽

Grey Area