| Title |
Anomaly Behavior Analysis of Smart Water Treatment Facility Service: Design, Analysis, and Evaluation |
| ID_Doc |
43454 |
| Authors |
Almazyad, I; Shao, SC; Hariri, S; Kholidy, HA |
| Title |
Anomaly Behavior Analysis of Smart Water Treatment Facility Service: Design, Analysis, and Evaluation |
| Year |
2023 |
| Published |
|
| DOI |
10.1109/AICCSA59173.2023.10479312 |
| Abstract |
The current trends toward the design and deployment of smart city services, including water services, improve quality, reliability and reduce operational costs. These advancements have led to the proliferation of ubiquitous connectivity to critical infrastructures. However, although smart sensors and Industrial Internet of Things (IIoTs) expedites rigorous monitoring and control, they exponentially increase vulnerabilities that can be exploited by cyberattacks. Therefore, development of advanced cybersecurity tools and resilience methods for smart city services are critically important because compromising these services can lead to disasters, accidents or even loss of life. To address the cybersecurity challenges facing smart city services, researchers need realistic testbeds to perform experiments, collect real-time data, and evaluate different security algorithms to protect smart critical infrastructure services. This paper presents a Water Treatment Facility Testbed (WTFT), a Cyber-Physical System (CPS) developed to enable experimentation with cybersecurity and resilient algorithms to deliver smart water services that can tolerate cyberattacks. Furthermore, an anomaly-based detection unit for water quality is implemented and our experimental results show a 96.8% F1-score, and a 98.3% accuracy with an attack detection latency under two seconds. |
| Author Keywords |
Drinking water treatment; machine learning; anomaly detection; cyber-physical system; cybersecurity |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:001222477900079 |
| WoS Category |
Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods |
| Research Area |
Computer Science |
| PDF |
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