scholarly journals Detection of Anomalies in Large-Scale Cyberattacks Using Fuzzy Neural Networks

AI ◽  
2020 ◽  
Vol 1 (1) ◽  
pp. 92-116 ◽  
Author(s):  
Paulo Vitor de Campos Souza ◽  
Augusto Junio Guimarães ◽  
Thiago Silva Rezende ◽  
Vinicius Jonathan Silva Araujo ◽  
Vanessa Souza Araujo
Keyword(s):  
Neural Networks ◽  
Computer Networks ◽  
Intelligent Systems ◽  
Large Scale ◽  
Fuzzy Neural Networks ◽  
Security Attacks ◽  
Massive Datasets ◽  
Fuzzy Neural ◽  
High Level ◽  
And Training

The fuzzy neural networks are hybrid structures that can act in several contexts of the pattern classification, including the detection of failures and anomalous behaviors. This paper discusses the use of an artificial intelligence model based on the association between fuzzy logic and training of artificial neural networks to recognize anomalies in transactions involved in the context of computer networks and cyberattacks. In addition to verifying the accuracy of the model, fuzzy rules were obtained through knowledge from the massive datasets to form expert systems. The acquired rules allow the creation of intelligent systems in high-level languages with a robust level of identification of anomalies in Internet transactions, and the accuracy of the results of the test confirms that the fuzzy neural networks can act in anomaly detection in high-security attacks in computer networks.

Pulsar Detection for Wavelets SODA and Regularized Fuzzy Neural Networks Based on Andneuron and Robust Activation Function

2019 ◽  
Vol 28 (01) ◽  
pp. 1950003 ◽  
Author(s):  
Paulo Vitor de Campos Souza ◽  
Luiz Carlos Bambirra Torres ◽  
Augusto Junio Guimarães ◽  
Vanessa Souza Araujo
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Activation Function ◽  
Fuzzy Neural Networks ◽  
Activation Functions ◽  
Large Scale Problems ◽  
Proposed Model ◽  
Fuzzy Neural ◽  
Learning Machine ◽  
Intelligent Models

The use of intelligent models may be slow because of the number of samples involved in the problem. The identification of pulsars (stars that emit Earth-catchable signals) involves collecting thousands of signals by professionals of astronomy and their identification may be hampered by the nature of the problem, which requires many dimensions and samples to be analyzed. This paper proposes the use of hybrid models based on concepts of regularized fuzzy neural networks that use the representativeness of input data to define the groupings that make up the neurons of the initial layers of the model. The andneurons are used to aggregate the neurons of the first layer and can create fuzzy rules. The training uses fast extreme learning machine concepts to generate the weights of neurons that use robust activation functions to perform pattern classification. To solve large-scale problems involving the nature of pulsar detection problems, the model proposes a fast and highly accurate approach to address complex issues. In the execution of the tests with the proposed model, experiments were conducted explanation in two databases of pulsars, and the results prove the viability of the fast and interpretable approach in identifying such involved stars.

Dynamic Controller Based on Ying Learning Algorithm for Medical Temperature System

2011 ◽  
Vol 187 ◽  
pp. 371-376
Author(s):  
Ping Zhang ◽  
Xiao Hong Hao ◽  
Heng Jie Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Neural Network ◽  
Learning Algorithm ◽  
Fuzzy Neural Networks ◽  
The Novel ◽  
Fuzzy Neural ◽  
Learning Set ◽  
And Training ◽  
Novel Algorithm

In order to avoid the over fitting and training and solve the knowledge extraction problem in fuzzy neural networks system. Ying Learning Dynamic Fuzzy Neural Network (YL-DFNN) algorithm is proposed. The Learning Set based on K-VNN is constituted from message. Then the framework of is designed and its stability is proved. Finally, Simulation indicates that the novel algorithm is fast, compact, and capable in generalization.

The min-max function differentiation and training of fuzzy neural networks

1996 ◽  
Vol 7 (5) ◽  
pp. 1139-1150 ◽  
Author(s):  
Xinghu Zhang ◽  
Chang-Chieh Hang ◽  
Shaohua Tan ◽  
Pei-Zhuang Wang
Keyword(s):  
Neural Networks ◽  
Fuzzy Neural Networks ◽  
Fuzzy Neural ◽  
And Training

scholarly journals Predicting Subcontractor Performance Using Web-Based Evolutionary Fuzzy Neural Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Chien-Ho Ko
Keyword(s):  
Neural Networks ◽  
Fuzzy Neural Networks ◽  
Remote Access ◽  
Cost Overruns ◽  
Web Based ◽  
Fuzzy Neural ◽  
Decision Making Processes ◽  
High Level ◽  
Prediction Mechanism ◽  
Future Status

Subcontractor performance directly affects project success. The use of inappropriate subcontractors may result in individual work delays, cost overruns, and quality defects throughout the project. This study develops web-based Evolutionary Fuzzy Neural Networks (EFNNs) to predict subcontractor performance. EFNNs are a fusion of Genetic Algorithms (GAs), Fuzzy Logic (FL), and Neural Networks (NNs). FL is primarily used to mimic high level of decision-making processes and deal with uncertainty in the construction industry. NNs are used to identify the association between previous performance and future status when predicting subcontractor performance. GAs are optimizing parameters required in FL and NNs. EFNNs encode FL and NNs using floating numbers to shorten the length of a string. A multi-cut-point crossover operator is used to explore the parameter and retain solution legality. Finally, the applicability of the proposed EFNNs is validated using real subcontractors. The EFNNs are evolved using 22 historical patterns and tested using 12 unseen cases. Application results show that the proposed EFNNs surpass FL and NNs in predicting subcontractor performance. The proposed approach improves prediction accuracy and reduces the effort required to predict subcontractor performance, providing field operators with web-based remote access to a reliable, scientific prediction mechanism.

HIGH-LEVEL FUZZY PETRI NETS AS A BASIS FOR MANAGING SYMBOLIC AND NUMERICAL INFORMATION

2000 ◽  
Vol 09 (04) ◽  
pp. 569-588 ◽  
Author(s):  
KEVIN F.R. LIU ◽  
JONATHAN LEE ◽  
WEILING CHIANG
Keyword(s):  
Neural Networks ◽  
Petri Nets ◽  
Fuzzy Set ◽  
Fuzzy Variable ◽  
Fuzzy Reasoning ◽  
Fuzzy Neural Networks ◽  
Numerical Information ◽  
Fuzzy Neural ◽  
Fuzzy Petri Nets ◽  
High Level

The focus of this paper is on an attempt towards a unified formalism to manage both symbolic and numerical information based on high-level fuzzy Petri nets (HLFPN). Fuzzy functions, fuzzy reasoning, and fuzzy neural networks are integrated in HLFPN In HLFPN model, a fuzzy place carries information to describe the fuzzy variable and the fuzzy set of a fuzzy condition. An arc is labeled with a fuzzy weight to represent the strength of connection between places and transitions. A fuzzy set and a fuzzy truth-value are attached to an uncertain fuzzy token to model imprecision and uncertainty. We have identified six types of uncertain transition: calculation transitions to compute functions with uncertain fuzzy inputs; inference transitions to perform fuzzy reasoning; neuron transitions to execute computations in neural networks; duplication transitions to duplicate an uncertain fuzzy token to several tokens carrying the same fuzzy sets and fuzzy truth values; aggregation transitions to combine several uncertain fuzzy tokens with the same fuzzy variable; and aggregation-duplication transitions to amalgamate aggregation transitions and duplication transitions. To guide the computation inside the HLFPN, an algorithm is developed and an example is used to illustrate the proposed approach.

The ECOS Framework and the ECO Learning Method for Evolving Connectionist Systems

1998 ◽  
Vol 2 (6) ◽  
pp. 195-202 ◽  
Author(s):  
Nikola Kasabov ◽  
Keyword(s):  
Neural Networks ◽  
Intelligent Systems ◽  
Input Data ◽  
Voice Recognition ◽  
Fuzzy Neural Networks ◽  
Person Identification ◽  
Fuzzy Neural ◽  
On Line ◽  
On Line Learning

This paper introduces a paradigm of connectionist architectures and an approach to building on-line, adaptive intelligent systems. This approach is called evolving connectionist systems (ECOS). EGOS evolve through incremental, on-line learning, both supervised and unsupervised. They can accommodate new input data, including new features, new classes, etc. The ECOS framework is presented and illustrated on a particular type of evolving neural networks - evolving fuzzy neural networks. The task of voice recognition and person identification is used as a case study.

Modeling of Self-Induced Vibrations that Occur During the Machining Process of Casting Patterns with the Use of The Fuzzy-Neural Networks Method

2013 ◽  
Vol 58 (3) ◽  
pp. 871-875
Author(s):  
A. Herberg
Keyword(s):  
Neural Networks ◽  
Control System ◽  
Network Structure ◽  
Fuzzy Systems ◽  
Fuzzy Neural Networks ◽  
Cnc Machining ◽  
Machining Process ◽  
Modeling Process ◽  
Fuzzy Neural ◽  
Takagi Sugeno

Abstract This article outlines a methodology of modeling self-induced vibrations that occur in the course of machining of metal objects, i.e. when shaping casting patterns on CNC machining centers. The modeling process presented here is based on an algorithm that makes use of local model fuzzy-neural networks. The algorithm falls back on the advantages of fuzzy systems with Takagi-Sugeno-Kanga (TSK) consequences and neural networks with auxiliary modules that help optimize and shorten the time needed to identify the best possible network structure. The modeling of self-induced vibrations allows analyzing how the vibrations come into being. This in turn makes it possible to develop effective ways of eliminating these vibrations and, ultimately, designing a practical control system that would dispose of the vibrations altogether.

Hotbox level detection of railway vehicle using fuzzy neural networks

2013 ◽  
Vol 33 (9) ◽  
pp. 2566-2569 ◽  
Author(s):  
Zhuanling CUI ◽  
Guoning LI ◽  
Sen LIN
Keyword(s):  
Neural Networks ◽  
Fuzzy Neural Networks ◽  
Railway Vehicle ◽  
Fuzzy Neural
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