Real-Time Identification of Cyber-Physical Attacks on Water Distribution Systems via Machine Learning–Based Anomaly Detection Techniques-Reference-Cited by-同舟云学术

Real-Time Identification of Cyber-Physical Attacks on Water Distribution Systems via Machine Learning–Based Anomaly Detection Techniques

Published:2019-01 Issue:1 Volume:145 Page:04018089
ISSN:0733-9496
Container-title:Journal of Water Resources Planning and Management
language:en
Short-container-title:J. Water Resour. Plann. Manage.

Author:

Abokifa Ahmed A.¹,Haddad Kelsey¹,Lo Cynthia²,Biswas Pratim³

Affiliation:

1. Ph.D. Candidate, Dept. of Energy, Environmental, and Chemical Engineering, Washington Univ. in St. Louis, 1 Brookings Dr., St. Louis, MO 63130.

2. Assistant Professor, Dept. of Energy, Environmental, and Chemical Engineering, Washington Univ. in St. Louis, 1 Brookings Dr., St. Louis, MO 63130.

3. Professor, Dept. of Energy, Environmental, and Chemical Engineering, Washington Univ. in St. Louis, 1 Brookings Dr., St. Louis, MO 63130 (corresponding author).

Publisher

American Society of Civil Engineers (ASCE)

Subject

Management, Monitoring, Policy and Law,Water Science and Technology,Geography, Planning and Development,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29WR.1943-5452.0001023

Reference46 articles.

1. Abokifa A. A. K. Haddad C. S. Lo and P. Biswas. 2017. “Detection of cyber physical attacks on water distribution systems via principal component analysis and artificial neural networks.” In Proc. World Environmental and Water Resources Congress 2017 676–691. Reston VA: ASCE.

2. Water quality modeling in the dead end sections of drinking water distribution networks

3. An Efficient Data-Driven Clustering Technique to Detect Attacks in SCADA Systems

4. Cyber Security of Water SCADA Systems—Part I: Analysis and Experimentation of Stealthy Deception Attacks

5. Cyber Security of Water SCADA Systems—Part II: Attack Detection Using Enhanced Hydrodynamic Models

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