SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach

Teixeira, Márcio Andrey; Salman, Tara; Zolanvari, Maede; Jain, Raj; Meskin, Nader; Samaka, Mohammed

doi:10.3390/fi10080076

Cited by 133 publications

(73 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In [31] and [32], we have investigated IDS design using ML and ANN models for securing the confidentiality and availability (reconnaissance and DoS attacks) of the system. However, in this paper, we have improved our testbed by adding the following elements.…”

Section: A Our Prior Workmentioning

confidence: 99%

Machine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things

Zolanvari

Teixeira

Gupta

et al. 2019

IEEE Internet Things J.

Self Cite

361

149

View full text Add to dashboard Cite

From 2012 to 2015, her research concerned performance improvement of communication networks. Since 2015, she has been a Graduate Research Assistant with Washington University in St. Louis. Her current research interests include utilizing machine learning and deep learning for network security of the Industrial Internet of Things, Internet of Things, machine learning, cyber-security, secure computer networks, and wireless communications. Marcio A. Teixeira (M'18-SM'18) received the M.Sc. degree in computer science and the Ph.D. degree in electrical engineering from the Federal

show abstract

Section: A Our Prior Workmentioning

confidence: 99%

Machine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things

Zolanvari

Teixeira

Gupta

et al. 2019

IEEE Internet Things J.

Self Cite

361

149

View full text Add to dashboard Cite

show abstract

“…Critical infrastructures (CI) such as the power system, oil and gas pipelines, water distribution, etc. are monitored and controlled by SCADA systems which links the CI together as a network through advanced Information Technologies (IT) [26]. As shown in Fig.…”

Section: ) Scada Network Vulnerabilities and Threatsmentioning

confidence: 99%

A Review of Machine Learning Approaches to Power System Security and Stability

2020

View full text Add to dashboard Cite

Increasing use of renewable energy sources, liberalized energy markets and most importantly, the integrations of various monitoring, measuring and communication infrastructures into modern power system network offer the opportunity to build a resilient and efficient grid. However, it also brings about various threats of instabilities and security concerns in form of cyberattack, voltage instability, power quality (PQ) disturbance among others to the complex network. The need for efficient methodologies for quicker identification and detection of these problems have always been a priority to energy stakeholders over the years. In recent times, machine learning techniques (MLTs) have proven to be effective in numerous applications including power system studies. In the literature, various MLTs such as artificial neural networks (ANN), Decision Tree (DT), support vector machines (SVM) have been proposed, resulting in effective decision making and control actions in the secured and stable operations of the power system. Given this growing trend, this paper presents a comprehensive review on the most recent studies whereby MLTs were developed for power system security and stability especially in cyberattack detections, PQ disturbance studies and dynamic security assessment studies. The aim is to highlight the methodologies, achievements and more importantly the limitations in the classifier(s) design, dataset and test systems employed in the reviewed publications. A brief review of reinforcement learning (RL) and deep reinforcement learning (DRL) approaches to transient stability assessment is also presented. Finally, we highlighted some challenges and directions for future studies.

show abstract

“…The performance of the proposed algorithm is measured by metrics derived from the confusion matrix [28,29]. The recognition of a license plate can only be considered correct if all characters that compose the license plate are correctly recognized.…”

Section: Evaluation Scenario and Performance Measurementsmentioning

confidence: 99%

A Real-Time Automatic Plate Recognition System Based on Optical Character Recognition and Wireless Sensor Networks for ITS

Dalarmelina

Teixeira

Meneguette

2019

Sensors

View full text Add to dashboard Cite

Automatic License Plate Recognition has been a recurrent research topic due to the increasing number of cameras available in cities, where most of them, if not all, are connected to the Internet. The video traffic generated by the cameras can be analyzed to provide useful insights for the transportation segment. This paper presents the development of an intelligent vehicle identification system based on optical character recognition (OCR) method to be used on intelligent transportation systems. The proposed system makes use of an intelligent parking system named Smart Parking Service (SPANS), which is used to manage public or private spaces. Using computer vision techniques, the SPANS system is used to detect if the parking slots are available or not. The proposed system makes use of SPANS framework to capture images of the parking spaces and identifies the license plate number of the vehicles that are moving around the parking as well as parked in the parking slots. The recognition of the license plate is made in real-time, and the performance of the proposed system is evaluated in real-time.

show abstract

SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach

Cited by 133 publications

References 19 publications

Machine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things

Machine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things

A Review of Machine Learning Approaches to Power System Security and Stability

A Real-Time Automatic Plate Recognition System Based on Optical Character Recognition and Wireless Sensor Networks for ITS

Contact Info

Product

Resources

About