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

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.

Analysis of the detection of an attack based on SQL injection using an impulse artificial neural network

This article presents the results of testing to create a specialized system that helps prevent cyberattacks, thus popularizing the construction of intelligent applications. Based on the results obtained, it can be argued that the tests carried out are satisfactory. The mathematical basis for building a neural network model is the HESADM model (Hybrid Artificial Intelligence Framework). The presented system allows you to form a set of rules using fuzzy logical neurons. This paper presents an approach to the formation of a fuzzy neural network used for detecting SQL injection attacks. The methodology used in this paper is an impulse artificial neural network (SANN), which uses an evolving neural network system (eCOS) and a multi-layer approach of an impulse artificial neural network to classify the exact type of intrusion or network anomaly with minimal computational potential. The impulse artificial neural system forms itself continuously, adapting to the input data, being in a functioning or not state, being under the supervision of an administrator. This system finds application to several other complex problems of the real world, proving its efficiency, including in the field of information security. The considered model is a hybrid evolving pulse anomaly detection model (HESADM), which works on impulses that occur in the system, while neurons are used to monitor the algorithm using a single training pass. In the system, traffic-oriented data is used by importing classes that use variable encoding. The data used is obtained by converting the real characteristics of network traffic into certain time stamps.

SQL Injection Attacks Prevention System Technology: Review

The vulnerabilities in most web applications enable hackers to gain access to confidential and private information. Structured query injection poses a significant threat to web applications and is one of the most common and widely used information theft mechanisms. Where hackers benefit from errors in the design of systems or existing gaps by not filtering the user's input for some special characters and symbols contained within the structural query sentences or the quality of the information is not checked, whether it is text or numerical, which causes unpredictability of the outcome of its implementation. In this paper, we review PHP techniques and other techniques for protecting SQL from the injection, methods for detecting SQL attacks, types of SQL injection, causes of SQL injection via getting and Post, and prevention technology for SQL vulnerabilities.

SQL Injection and Areas of Security Concern

Cyber-crimes are growing rapidly and to prevent these crimes one should share all the knowledge he/she has to make people aware of these attacks. In the field of Application Security there is a very well-known vulnerability ―SQL INJECTION‖. In this paper, we have focused on what are the type of SQL Injection attacks and where it can be found in any application.