Towards self-organizing maps based Computational Intelligent System for denial of Service Attacks Detection

2010 ◽  
Author(s):  
M.A. Perez-del-Pino ◽  
P. Garcia Baez ◽  
P. Fernandez Lopez ◽  
C.P. Suarez Araujo
Keyword(s):  
Intelligent System ◽  
Denial Of Service ◽  
Denial Of Service Attacks ◽  
Self Organizing Maps ◽  
Computational Intelligent ◽  
Self Organizing

Self-Organizing Maps for Early Detection of Denial of Service Attacks

2012 ◽  
pp. 195-219 ◽  
Author(s):  
Miguel Ángel Pérez del Pino ◽  
Patricio García Báez ◽  
Pablo Fernández López ◽  
Carmen Paz Suárez Araujo
Keyword(s):  
Early Detection ◽  
Denial Of Service ◽  
Denial Of Service Attacks ◽  
Self Organizing Maps ◽  
Self Organizing

scholarly journals Computational Intelligent Approaches for Non-Technical Losses Management of Electricity

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2393
Author(s):  
Rubén González Rodríguez ◽  
Jamer Jiménez Mares ◽  
Christian G. Quintero M.
Keyword(s):  
Intelligent System ◽  
Moving Average ◽  
Demand Forecasting ◽  
Real Data ◽  
Electrical Energy ◽  
Caribbean Region ◽  
Self Organizing Maps ◽  
Moving Average Models ◽  
Intelligent Approach ◽  
Computational Intelligent

This paper presents an intelligent system for the detection of non-technical losses of electrical energy associated with the fraudulent behaviors of system users. This proposal has three stages: a non-supervised clustering of consumption profiles based on a hybrid algorithm between self-organizing maps (SOM) and genetic algorithms (GA). A second stage for demand forecasting is based on ARIMA (autoregressive integrated moving average) models corrected intelligently through neural networks (ANN). The final stage is a classifier based on random forests for fraudulent user detection. The proposed intelligent approach was trained and tested with real data from the Colombian Caribbean region, where the utility reports energy losses of around 18% of the total energy purchased by the company during the five last years. The results show an average overall performance of 82.9% in the detection process of fraudulent users, significantly increasing the effectiveness compared to the approaches (68%) previously applied by the utility in the region.

scholarly journals Self-Organization and Adaptation in Intelligent Systems

1998 ◽  
Vol 2 (6) ◽  
pp. 177-177
Author(s):  
Nikola Kasabov ◽  
◽  
Robert Kozma ◽  
Keyword(s):  
Neural Networks ◽  
Information Systems ◽  
Intelligent Systems ◽  
Adaptive Systems ◽  
Hebbian Learning ◽  
Intelligent System ◽  
Self Organizing Maps ◽  
Intelligent Information Systems ◽  
Intelligent Information ◽  
Self Organizing

This special issue is devoted to one of the important topics of current intelligent information systems-their ability to adapt to the environment they operate in, as adaptation is one of the most important features of intelligence. Several milestones in the literature on adaptive systems mark the development in this area. The Hebbian learning rule,1) self-organizing maps,2,3) and adaptive resonance theory4) have influenced the research in this area a great deal. Some current development suggests methods for building adaptive neurofuzzy systems,5) and adaptive self-organizing systems based on principles from biological brains.6) The papers in this issue are organized as follows: The first two papers present material on organization and adaptation in the human brain. The third paper, by Kasabov, presents a novel approach to building open structured adaptive systems for on-line adaptation called evolving connectionist systems. The fourth paper by Kawahara and Saito suggests a method for building virtually connected adaptive cell structures. Papers 5 and 6 discuss the use of genetic algorithms and evolutionary computation for optimizing and adapting the structure of an intelligent system. The last two papers suggest methods for adaptive learning of a sequence of data in a feed-forward neural network that has a fixed structure. References: 1) D.O. Hebb, "The Organization of Behavior," Jwiley, New York, (1949). 2) T. Kohonen, "Self-organisation and associative memory," Springer-Verlag, Berlin, (1988). 3) T. Kohonen, "Self-Organizing Maps, second edition," Springer Verlag, (1997). 4) G. Carpenter and S. Grossberg, "Pattern recognition by self-organizing neural networks," The MIT Press, Cambridge, Massachusetts, (1991). 5) N. Kasabov, "Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering," The MIT Press, CA, MA, (1996). 6) S. Amari and N. Kasabov "Brain-like Computing and Intelligent Information Systems," Springer Verlag, Singapore, (1997).

scholarly journals Using Self-Organizing Architectures to Mitigate the Impacts of Denial-of-Service Attacks on Voltage Control Schemes

2019 ◽  
Vol 10 (3) ◽  
pp. 3010-3019 ◽  
Author(s):  
Calum Cameron ◽  
Charalampos Patsios ◽  
Phil C. Taylor ◽  
Zoya Pourmirza
Keyword(s):  
Denial Of Service ◽  
Voltage Control ◽  
Denial Of Service Attacks ◽  
Control Schemes ◽  
Self Organizing
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