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PSO Supported Ensemble Algorithm for Bad Data Detection Against Intelligent Hacking Algorithm
- Source :
- Frontiers in Energy Research, Vol 9 (2021)
- Publication Year :
- 2021
- Publisher :
- Frontiers Media SA, 2021.
-
Abstract
- Power system cybersecurity have recently become important due to cyber-attacks. Due to Advanced computer science and machine learning (ML) applications have being used by malicious attackers; cybersecurity is more crucial to create sustainable, reliable, efficient and well-protected cyber-systems. Considering the fact that it is required for power system operators to pay more attention to develop sophisticated detection mechanism. In this study, a novel machine learning based detection algorithm that combines five most popular ML algorithms with Particle Swarm Optimizer (PSO) is developed and tested by using intelligent hacking algorithm that is specially developed to measure of effectives of this study. The hacking algorithm provides three different types of injections: random, continuous random and slowly injections by adaptive manner, so that detection would be harder. Results shows that recall values with proposed algorithm for each different type of attacks have been increased.
- Subjects :
- Power system operators
Economics and Econometrics
Boosting (machine learning)
linear discriminant analysis
Computer science
020209 energy
Particle swarm optimizer
Energy Engineering and Power Technology
02 engineering and technology
Measure (mathematics)
General Works
bad data detection
Electric power system
0202 electrical engineering, electronic engineering, information engineering
support vector machine
Hacker
Renewable Energy, Sustainability and the Environment
logistic regression
Data detection
k-nearest neighbor
machine learning
Fuel Technology
020201 artificial intelligence & image processing
Algorithm
hacking mechanism
Subjects
Details
- Language :
- English
- ISSN :
- 2296598X
- Volume :
- 9
- Database :
- OpenAIRE
- Journal :
- Frontiers in Energy Research
- Accession number :
- edsair.doi.dedup.....8fbdbd561296c1b07904652ce3e0fa11
- Full Text :
- https://doi.org/10.3389/fenrg.2021.649460