Machine learning for power system disturbance and cyber-attack discrimination
By Raymond Borges Hink, Justin M. Beaver, Mark A. Buckner, Thomas H. Morris, Uttam Adhikari, Shengyi Pan. 2014 7th International Symposium on Resilient Control Systems (ISRCS), 2014
297 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Networks and Communications, Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/6ef10dfe5cc86df24a09df0a64853bcbb029c0f0
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Asaf Shabtai
- Moshe Kravchik
- Jonathan Goh
- Raj Jain
- Nader Meskin
- Ernesto Damiani
- Zhibo Zhang
- Fatma Taher
In Computer Networks and Communications
- Asaf Shabtai
- Moshe Kravchik
- Jonathan Goh
- Raj Jain
- Ernesto Damiani
- Zhibo Zhang
- Fatma Taher
- Chan Yeob Yeun
In Control and Systems Engineering
- Asaf Shabtai
- Moshe Kravchik
- Jonathan Goh
- Angelos Angelopoulos
- T. Zahariadis
- Panagiotis Trakadas
- Nikolaos Nomikos
- Emmanouel T. Michailidis
Citations in the corpus
- Developing a Hybrid Intrusion Detection System Using Data Mining for Power Systems (2015, methodology): “The binary and three-class methods in [22] lead to improved accuracy at the expense of classification precision.”
- Anomaly Detection in Cyber Physical Systems Using Recurrent Neural Networks (2017, background): “In the supervised approach ([4], [7], [8]), training labelled data that comprises of both normal and abnormal behaviours are provided to the model to learn.”
- Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research (2022, methodology): “[120] Power System Attack Datasets 2014 248 This dataset consists of three datasets that measure the normal, disturbed, controlled, and cyberattack behaviors of the electric transmission system.”
- Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects (2019, methodology): “0 data sets are not available, the evaluation of the threat intelligence scheme was realised by combining two well-known data sets; the CPS data set of sensors and physical devices [85] and the UNSW-NB15 data set of network traffic [86].”
- Detecting Cyber Attacks in Industrial Control Systems Using Convolutional Neural Networks (2018, methodology): “Various machine learning techniques are used in ICS anomaly detection, including support vector machines [31] [15], random forest [1], and artificial neural networks [29], [4].”
- Cybersecurity for industrial control systems: A survey (2020, background): “[33] have demonstrated the use of machine learning techniques for power system disturbance and cyber-attack discrimination.”