scholarly journals Methodology for constructing a neural fuzzy network in the field of information security

2021 ◽  
pp. 43-56
Author(s):  
Anastasiya Arkhipova ◽  
◽  
Pavel Polyakov ◽  
Keyword(s):  
Network Architecture ◽  
Fuzzy Inference ◽  
Activation Function ◽  
Fuzzy Rules ◽  
Sql Injection ◽  
Inference System ◽  
Component Structure ◽  
Training Models ◽  
Injection Attacks ◽  
Sql Injection Attacks

This paper proposes the use of hybrid models based on neural networks and fuzzy systems to build intelligent intrusion detection systems based on the theory of fuzzy rules. The presented system will be able to generate rules based on the results using fuzzy logic neurons. To avoid oversaturation and assist in determining the necessary network topology, training models based on extreme learning machine and regularization theory will be used to find the most significant neurons. In this paper, a type of SQL injection cyberattack is considered, which actively exploits errors in systems that communicate with the database via SQL commands, and for this reason is considered a kind of straightforward attack. The fuzzy neural network architecture used in detecting SQL injection attacks is a multi-component structure. The first two layers of the model are considered as a fuzzy inference system capable of extracting knowledge from data and transforming it into fuzzy rules. These rules help build automated systems for detecting SQL injection attacks. The third layer consists of a simple neuron that has an activation function called a leaky ReLU. The first layer consists of fuzzy neurons, the activation functions of which are Gaussian membership functions of fuzzy sets, defined in accordance with the partitioning of the input variables. The technique uses the concept of a simple linear regression model to solve the problem of choosing the best subsets of neurons. To perform model selection, the paper used the widely used least angular regression (LARS) algorithm.

PERFORMANCE EVALUATION OF FUZZY SINGLE LAYER WEIGHTLESS NEURAL NETWORK

2007 ◽  
Vol 15 (03) ◽  
pp. 381-393 ◽  
Author(s):  
RAIDA AL-ALAWI
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Fuzzy Inference ◽  
Single Layer ◽  
Learning Phase ◽  
Fuzzy Rules ◽  
Test Bed ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Fuzzy Pattern

The paper evaluates the performance of a neuro-fuzzy pattern classification system based on the weightless neural network architecture. The system utilizes a Single Layer Weightless Neural Network (SLWNN) to extract the features vector that measures the similarity of the input pattern to the different classification groups. In contrast to the traditional crisp Winner-Takes-All (WTA) classification scheme used by SLWNN, our system uses a Fuzzy Inference System (FIS) for classification. The network is trained by a hybrid learning scheme that combines a single pass learning phase for training the SLWNN followed by a supervised learning phase for extracting a set of fuzzy rules suitable to classify the training set. The FIS learns fuzzy rules from the feature vectors generated by the SLWNN for the set of training patterns. The recognition of handwritten numerals is employed as a test-bed to demonstrate the effectiveness of the proposed neuro-fuzzy system. Experimental results show that the performance of the proposed system surpasses the performance of the traditional SLWNN.

scholarly journals Aplikasi Pengambilan Keputusan dengan Metode Tsukamoto pada Penentuan Tingkat Kepuasan pelanggan

CAUCHY ◽  
2015 ◽  
Vol 4 (1) ◽  
pp. 10 ◽  
Author(s):  
Venny Riana Riana Agustin ◽  
Wahyu Henky Irawan
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Weighted Average ◽  
Fuzzy Rules ◽  
Quality Of Services ◽  
Inference System ◽  
A Value ◽  
Fuzzy Logic Analysis

Tsukamoto method is one method of fuzzy inference system on fuzzy logic for decision making. Steps of the decision making in this method, namely fuzzyfication (process changing the input into kabur), the establishment of fuzzy rules, fuzzy logic analysis, defuzzyfication (affirmation), as well as the conclusion and interpretation of the results. The results from this research are steps of the decision making in Tsukamoto method, namely fuzzyfication (process changing the input into kabur), the establishment of fuzzy rules by the general form IF a is A THEN B is B, fuzzy logic analysis to get alpha in every rule, defuzzyfication (affirmation) by weighted average method, as well as the conclusion and interpretation of the results. On customers at the case, in value of 16 the quality of services, the value of 17 the quality of goods, and value of 16 a price, a value of the results is 45,29063 and the level is low satisfaction

scholarly journals Retrofitting Existing Web Applications with Effective Dynamic Protection Against SQL Injection Attacks

2010 ◽  
Vol 1 (1) ◽  
pp. 20-40 ◽  
Author(s):  
San-Tsai Sun ◽  
Konstantin Beznosov
Keyword(s):  
Web Applications ◽  
False Positives ◽  
Sql Injection ◽  
Injection Attacks ◽  
Track Parameter ◽  
Effective Dynamic ◽  
Protection Mechanisms ◽  
Sql Injection Attacks ◽  
Application Developers ◽  
Parameter Values

This article presents an approach for retrofitting existing Web applications with run-time protection against known, as well as unseen, SQL injection attacks (SQLIAs) without the involvement of application developers. The precision of the approach is also enhanced with a method for reducing the rate of false positives in the SQLIA detection logic, via runtime discovery of the developers’ intention for individual SQL statements made by Web applications. The proposed approach is implemented in the form of protection mechanisms for J2EE, ASP.NET, and ASP applications. Named SQLPrevent, these mechanisms intercept HTTP requests and SQL statements, mark and track parameter values originating from HTTP requests, and perform SQLIA detection and prevention on the intercepted SQL statements. The AMNESIA testbed is extended to contain false-positive testing traces, and is used to evaluate SQLPrevent. In our experiments, SQLPrevent produced no false positives or false negatives, and imposed a maximum 3.6% performance overhead with 30 milliseconds response time for the tested applications.

scholarly journals Modeling of Stage-Discharge Using Back Propagation ANN, ANFIS, and WANN-based Computing Technique

2021 ◽  
Author(s):  
Ravi Shukla ◽  
Pravendra Kumar ◽  
Dinesh Kumar Vishwakarma ◽  
Rawshan Ali ◽  
Rohitashw Kumar ◽  
...  
Keyword(s):  
Water Resource Management ◽  
Hydrological Modeling ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Activation Function ◽  
Neural System ◽  
Back Propagation Algorithm ◽  
Inference System ◽  
Computing Technique ◽  
Artificial Neural

Abstract The development of the stage-discharge relationship is a fundamental issue in hydrological modeling. Due to the complexity of the stage-discharge relationship, discharge prediction plays an essential role in planning and water resource management. The present study was conducted for modeling of discharge at the Gaula barrage site in Uttarakhand state of India. The study evaluated, Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN) and Wavelet-Based Artificial Neural System (WANN) based models to estimate the discharge. The daily data of 12 years (2007-2018) were used to train and test the models. The Gamma test was used to identify the best model for discharge prediction. The input data having a stage with one-day lag and discharge with one and two-days lag and current-day discharge as output was used for discharge modeling. In the case of ANN models, the back-propagation algorithm and hyperbolic tangent sigmoid activation function was used. WANN used Haar, a trous based wavelet function. In ANFIS models, triangular, psig, generalized bell, and Gaussian membership functions were used to train and test the models. The models were evaluated qualitatively and quantitatively using correlation coefficient, root means square error, Willmott index, and coefficient of efficiency. It was found that ANFIS model performed better than ANN and WANN-based models for discharge prediction at the Gaula barrage.

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