Method of protection of personal data in its processing in information system based on the artificial neural network

2021 ◽  
Vol 4 ◽  
pp. 54-59
Author(s):  
I. S. Kozin ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Information System ◽  
Information Security ◽  
User Behavior ◽  
Personal Data ◽  
Access Time ◽  
User Characteristics ◽  
Time Duration ◽  
Artificial Neural

One of the most actively developing areas of information security is the User Behavior Analytics. This paper presents a method of detecting anomalies in the behavior of an information system user has been developed, based on the use of an artificial neural network that signals the commission of illegal actions. Users behavior characteristics had been offered to use sample input values: access time; duration of work performed; place of access; a set of data with which the user works; list of actions taken. An approach to assigning numeric values to user characteristics is proposed, based on the fuzzy set theory and One-Hot Encoding method. Method provides more effective detecting abnormalities in user behavior than analyze by information security specialist without using the special automation tools.

scholarly journals Adaptation of the Diagnostic Artificial Neural Network Structure When New Training Examples Appear

2020 ◽  
Vol 6 (4) ◽  
pp. 120-126
Author(s):  
A. Malikov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Information Security ◽  
Classification Problem ◽  
Data Set ◽  
Neural Network Structure ◽  
Artificial Neural ◽  
Training Examples ◽  
Information Image

In this paper we can see that identified computer incidents are subject for diagnostics, during which the characteristics of information security violations are clarified (purpose, causes, consequences, etc.). To diagnose computer incidents, we can use methods of automation while collection and processing the events that occur as a result of the implementation of scenarios for information security violations. Artificial neural networks can be used to solve the classification problem of assigning diagnostic data set (information image of a computer incident) to one of the possible values of the violation characteristic. The purpose of this work is to adapt the structure of an artificial neural network that allows the accuracy diagnostics of computer incidents when new training examples appear.

scholarly journals Social Vulnerability Assessment Using Artificial Neural Network (ANN) Model for Earthquake Hazard in Tabriz City, Iran

2018 ◽  
Vol 10 (10) ◽  
pp. 3376 ◽  
Author(s):  
Mohsen Alizadeh ◽  
Esmaeil Alizadeh ◽  
Sara Asadollahpour Kotenaee ◽  
Himan Shahabi ◽  
Amin Beiranvand Pour ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Information System ◽  
Geographic Information System ◽  
Social Vulnerability ◽  
Ann Model ◽  
Scatter Plots ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Tabriz City

This study presents the application of an artificial neural network (ANN) and geographic information system (GIS) for estimating the social vulnerability to earthquakes in the Tabriz city, Iran. Thereby, seven indicators were identified and used for earthquake vulnerability mapping, including population density, household density, employed density, unemployed density, and literate people. To obtain more accuracy in our analysis, all of the indicators were entered into a geographic information system (GIS). After the standardization of the data, an artificial neural network (ANN) model was applied for deriving a social vulnerability map (SVM) of different hazard classes for Tabriz city. The results showed that 0.77% of the total area was found to be very highly vulnerable. Very low vulnerability was recorded for 76.31% of the total study area. The comparison of data provided by (SVM) and the residential building vulnerability (RBV) of Tabriz city indicated the validity of the results obtained by ANN processes. Scatter plots are used to plot the data. These scatter plots indicate the existence of a strong positive relationship between the most vulnerable zones (1, 4, and 5) and the least (3, 7, and 9) of the SVM and RBV. The results highlight the importance of using social vulnerability study for defining seismic-risk mitigation policies, emergency management, and territorial planning in order to reduce the impacts of disasters.

Preventing food waste in subsidy-based university dining systems: An artificial neural network-aided model under uncertainty

2021 ◽  
pp. 0734242X2110179
Author(s):  
Mohammadali Faezirad ◽  
Alireza Pooya ◽  
Zahra Naji-Azimi ◽  
Maryam Amir Haeri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Food Waste ◽  
Prediction Models ◽  
User Behavior ◽  
Total Cost ◽  
Penalty Cost ◽  
Delivery Times ◽  
Artificial Neural ◽  
Prediction Approach

Food waste planning at universities is often a complex matter due to the large volume of food and variety of services. A major portion of university food waste arises from dining systems including meal booking and distribution. Although dining systems have a significant role in generating food wastes, few studies have designed prediction models that could control such wastes based on reservation data and behavior of students at meal delivery times. To fill this gap, analyzing meal booking systems at universities, the present study proposed a new model based on machine learning to reduce the food waste generated at major universities that provide food subsidies. Students’ reservation and their presence or absence at the dining hall (show/no-show rate) at mealtime were incorporated in data analysis. Given the complexity of the relationship between the attributes and the uncertainty observed in user behavior, a model was designed to analyze definite and random components of demand. An artificial neural network-based model designed for demand prediction provided a two-step prediction approach to dealing with uncertainty in actual demand. In order to estimate the lowest total cost based on the cost of waste and the shortage penalty cost, an uncertainty-based analysis was conducted at the final step of the research. This study formed a framework that could reduce the food waste volume by up to 79% and control the penalty and waste cost in the case study. The model was investigated with cost analysis and the results proved its efficiency in reducing total cost.

scholarly journals Model and method for diagnosing computer incidents in information and communication systems based on deep machine learning

2019 ◽  
pp. 32-42
Author(s):  
Albert Malikov ◽  
Vladimir Avramenko ◽  
Igor Saenko
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Information Security ◽  
Communication System ◽  
Communication Systems ◽  
Security Breach ◽  
Main Requirement ◽  
Information And Communication ◽  
Artificial Neural ◽  
Hidden Layer

Introduction: Models and methods for diagnosing computer incidents recorded in information and communication systems are the most important components in mathematical support of information security systems. The main requirement for the diagnostics is prompt identification of security violation characteristics. This problem is complicated due to the amount and variability of the initial data on information security violation. Purpose: Development of a model for diagnosing a computer incident, along with a method which would allow you to quickly determine the characteristics of a security violation. Results: Security breach characteristics important for making a decision about responding to an identified computer incident can be determined via deep artificial neural networks. A structural feature of the proposed deep artificial neural network is combining the coding part of the autoencoder and a multilayer perceptron. In addition, the method implements a parallel mode of processing information events which have occurred in the information and communication system before the incident was detected, by using a separate proposed artificial neural network for each secondary characteristic of the security breach. The method of determining the values of these secondary characteristics allows you to greatly improve the diagnostics efficiency, having acceptable values of precision and recall for the security violation characteristics to determine. The dependence has been studied of the completeness and classification accuracy on the number of neurons in the hidden layer. A sufficient number of neurons in the hidden layer for achieving the required training efficiency is experimentally determined. Practical relevance: The developed model and method can be implemented using standard software and hardware (servers) of an information and communication system. Their combined use with the existing models and methods of monitoring and diagnostics can significantly improve the efficiency of an information security system.

scholarly journals Seismotectonics Considered Artificial Neural Network Earthquake Prediction in Northeast Seismic Region of China

2015 ◽  
Vol 9 (1) ◽  
pp. 522-528 ◽  
Author(s):  
Jian Sheng ◽  
Dongmei Mu ◽  
Hongyan Zhang ◽  
Han Lv
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Earthquake Prediction ◽  
Research Area ◽  
Ann Model ◽  
Time Duration ◽  
Seismic Region ◽  
Prediction Time ◽  
Input Parameters ◽  
Artificial Neural

It is well known that earthquakes are a regional event, strongly controlled by local geological structures and circumstances. Reducing the research area can reduce the influence of other irrelevant seismotectonics. A new sub regiondividing scheme, considering the seismotectonics influence, was applied for the artificial neural network (ANN) earthquake prediction model in the northeast seismic region of China (NSRC). The improved set of input parameters and prediction time duration are also discussed in this work. The new dividing scheme improved the prediction accuracy for different prediction time frames. Three different research regions were analyzed as an earthquake data source for the ANN model under different prediction time duration frames. The results show: (1) dividing the research region into smaller subregions can improve the prediction accuracies in NSRC, (2) larger research regions need shorter prediction durations to obtain better performance, (3) different areas have different sets of input parameters in NSRC, and (4) the dividing scheme, considering the seismotectonics frame of the region, yields better results.

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