Fuzzy neural networks to create an expert system for detecting attacks by SQL Injection

Batista, Lucas Oliveira; Betim, Faculdade Una de; Silva, Gabriel; Araújo, Vanessa Souza; Araújo, Vinícius Jonathan Silva; Rezende, Thiago Silva; Guimarães, Augusto Júnio; Souza, Paulo Vitor de Campos

doi:10.5769/j201801001

Cited by 26 publications

(14 citation statements)

References 26 publications

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“…-In 2018, Murali Ravuri [15], with other researchers as the starting point for combining expert knowledge with knowledge acquired from a different data store, such as electronic health records methods, to combine approaches to improve accuracy and flexibility. In the same year, Batista L. Oliveira [16] combined three techniques with a knowledge base system, fuzzy, connectionist with SQL instruction to detect attacks and increase the concern with the security of this environment. The methods called to conclude the results of the experiments prove to construct a prototype system to encourage the development of intelligent apps in order to avoid cyber-attacks.…”

Section: Related Workmentioning

confidence: 99%

Design Hybrid Intelligent system for Cattle Diseases Diagnosis and Treatment

Hasoon¹

2022

Technium

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Cattle is one of the main concerns of people and companies in various countries of the world. This paper built the architecture of the cattle diseases by combining the characteristics of an expert system with a neural network Expert system focuses on diagnosing the possible diseases that affect the life of cattle through constructing knowledge bases. While the Radial base functions, a neural network is proposed for the cattle diseases treatments. A tightly coupled model is used to implement a hybrid intelligence technique for diagnosing and Treatment cattle diseases. The result generated by the system was enough to demonstrate its performance.

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Section: Related Workmentioning

confidence: 99%

Design Hybrid Intelligent system for Cattle Diseases Diagnosis and Treatment

Hasoon¹

2022

Technium

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“…To decrease this exponential relationship and facilitate pseudoinverse calculations. In the models of de Campos Souza et al [68,71] Guimaraes et al [34] and Batista et al [10], resampling regularization techniques were used.…”

Section: Pruning Techniquementioning

confidence: 99%

Autonomous Data Density pruning fuzzy neural network for Optical Interconnection Network

et al. 2020

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Traditionally, fuzzy neural networks have parametric clustering methods based on equally spaced membership functions to fuzzify inputs of the model. In this sense, it produces an excessive number calculations for the parameters' definition of the network architecture, which may be a problem especially for realtime large-scale tasks. Therefore, this paper proposes a new model that uses a nonparametric technique for the fuzzification process. The proposed model uses an autonomous data density approach in a pruned fuzzy neural network, wich favours the compactness of the model. The performance of the proposed approach is evaluated through the usage of databases related to the Optical Interconnection Network. Finally, binary patterns classification tests for the identification of Temporal Distribution (Asynchronous or Client-Server) were performed and compared with stateof-the-art fuzzy neural-based and traditional machine learning approaches. Results demonstrated that the proposed model is an efficient tool for these challenging classification tasks.

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“…The fuzzy neural networks have been used recently in several problems of science. These hybrid models have outstanding performance in problem detection, and extraction of fuzzy rules in the field of breast cancer [63], Pulsar detection [64] and SQL Injection attacks [65]. Therefore, this model has outstanding and extremely varied performance in solving complex problems, regardless of the nature of the problem.…”

Section: Fuzzy Neural Network Modelsmentioning

confidence: 99%

A hybrid approach of intelligent systems to help predict absenteeism at work in companies

et al. 2019

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In recent years, several surveys have been conducted on absenteeism and how this affects the routine of conducting productive operations in companies. Therefore, having criteria for predicting absenteeism at work can help managers in contingency actions reduce financial losses due to the absence of a worker in their workplace. The objective of this work is to apply the artificial intelligence concepts of a regularized fuzzy neural network, which combines the benefits of artificial neural networks with the fuzzy set theory to obtain more accurate results in predicting corporate absenteeism. The database called absenteeism at work, taken from the UCI Machine Learning Repository, which captured elements of a Brazilian company, was applied in a fuzzy neural network model that allows the calculation of the regressors, defining the estimate of the lack of hours of an employee. The results of the experiments prove that the intelligent model can help in the creation of a specialist system that assists in the prediction of absenteeism.

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Fuzzy neural networks to create an expert system for detecting attacks by SQL Injection

Cited by 26 publications

References 26 publications

Design Hybrid Intelligent system for Cattle Diseases Diagnosis and Treatment

Design Hybrid Intelligent system for Cattle Diseases Diagnosis and Treatment

Autonomous Data Density pruning fuzzy neural network for Optical Interconnection Network

A hybrid approach of intelligent systems to help predict absenteeism at work in companies

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