This paper describes the problem of task distribution received through the electronic document management system. The fuzzy-production model underlying the solution to this problem is described. Based on the proposed model, a software package was developed for decision-making support of task performers selection, its structure is presented. A model of task distribution is considered taking into account its forecast values. The basic steps in forecasting model construction are described. The effectiveness of this approach for task distribution, based on workload indicators of specialists with different levels of working capacity and qualifications, is shown.

Models and Methods of Forecasting and Tasks Distribution by Performers in Electronic Document Management Systems

A R T I C L E I N F O A B S T R A C T Article history: Received: 13 January, 2020 Accepted: 16 March, 2020 Online: 04 April, 2020 Bull service is the natural copulation by a purebred male carabao with a female counterpart. This is part of the bull loan agenda of the Philippine Carabao Center-Visayas State University (PCC-VSU), one of the 12 regional centers of PCC. For the past years, PCC-VSU used averaging of bull services count of previous years and sometimes did a little tweaking of the formulas in setting annual targets. Some targets fell short in the actual accomplishment that may have led to achieving lesser animal products. There is a need for resource optimization. Resource optimization through Auto-Regressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) forecasting can be used in the setting of goals of future number of copulations. This study deals with the comparative assessment of ARIMA and ANN in 4-step and 12-step ahead time series forecasting of bull services. This endeavor dealt with quarterly data from 2002 to 2017. In ARIMA, the for 4step ahead (1-year) forecasting had the data from 2002 to 2016 for training while data from 2017 was employed for testing. ARIMA 12-step ahead (3-year) forecasting had data from 2002 to 2014 for training and 2015 to 2017 for testing. The ANN 4-step ahead prediction made use of data from 2002 to 2015 for training, 2016 for validation and 2017 for testing. ANN 12-step forecasting had data from 2002 to 2011 for training, 2012 to 2014 for validation and 2015 to 2017 for testing. R studio, a tool for statistical computing was employed in the analysis. Based on the performance measures Root Mean Square Error, Mean Absolute Percent Error and Mean Absolute Error the ANN outshines the ARIMA in both 4-step and 12-step ahead forecasting. The ANN forecasting can surely help in optimizing the center’s resources. The forecasting of bull services using multivariate series is recommended.

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A Comparative Analysis of ARIMA and Feed-Forward Neural Network Prognostic Model for Bull Services

This paper discusses the concept and problem of detecting network scanning attacks and describes the targets of network scanning attacks. The main attack methods and approaches to scanning network ports are considered. Intrusion detection systems (IDS) are used to detect network scanning attacks. Based on the method of detecting attacks, such systems are divided into IDS, which detects attacks based on signatures, and IDS, which detects attacks based on anomalies. In practice, it is recommended that these IDS detection methods be used together. It is proposed to use a trained neural network as a tool for detecting network scanning attacks. The implementation of the neural network required to prepare the initial data for training, to determine the parameters of the network, to conduct training, and to evaluate the results of its testing. When developing a neural network model, data from the publicly available set \"NSLKDD\" were used. During data processing, entries that were not related to network scanning attacks were removed from the original NSL-KDD set. After processing the initial data, the sample contained 5108 records, 3379 of which characterized normal connections, and 1729 connections were related to network scanning attacks. The Deductor modeling environment was used to build a neural network model. The structure of the constructed neural network was as follows: 11 input neurons, 1 output neuron, and one hidden layer consisting of 23 neurons. The neural network was trained using an error backpropagation algorithm. The quality of the neural network model was assessed using contingency tables with the calculation of the classification accuracy, as well as errors of the first and second kind. The values of these errors turned out to be insignificant. The constructed neural network model revealed most of the connections characterizing network scanning attacks. The neural network assessment confirmed its adequacy and the possibility of effective practical use for detecting network scanning attacks. Keywordsnetwork scanning attack, information security, data mining, neural network, neural network model.

/pdf/neural-network-model-for-detecting-network-scanning-attacks-4bgtmqlo00.pdf

Neural Network Model for Detecting Network Scanning Attacks

This paper solves the problem of defining phishing sites based on building a team model of fuzzy neural networks (FNNs). The main methods of phishing are analyzed. Attention is drawn to the fact that phishing has become widespread on the Internet through the use of phishing sites. The expediency of identifying phishing sites based on the analysis of their URLs is noted. The main approaches to identifying phishing sites are described. The need toimplement an approach based on machine learning by constructing fuzzy neural networks for the creation of fuzzy knowledge bases and their use to identify phishing sites is actualized. Automating the identification of phishing sites based on the neuro-fuzzy approach required solving the problems of collecting and preparing initial data for analysis,building a team model of fuzzy neural networks, and forming a fuzzy knowledge base, as well as conducting research, and assessing the accuracy of identifying phishing sites based on the constructed model. The initial data was formed from various sources. The total amount of initial data was 50,000. Of these, 10 input features for analysis were selected by an expert. After carrying out the correlation analysis, 4 most informative input features were selected for analysis: site lifetime, site rank, URL length, and the registered status of the site. An output feature of the site was its type: phishing or legitimate. After assessing the quality and cleaning the selected data, the resulting sample was formed of 34718 rows, of which 70% were used for learning (24303 rows), and 30% (10415 rows) for testing. A team model offuzzy neural networks was built and a knowledge base was formed on the basis of the data obtained, including 4608 fuzzy rules. Studies have shown that the number of errors of the 1st type in identifying phishing sites is 2.01%, and 2.89% for errors of the 2nd type. The general classification error based on knowledge base rules is 4.9%. The accuracy of identifying phishing sites was 95.1%, which exceeds the accuracy of other classification methods: multilayer neural network, decision tree, linear and logistic regression. The knowledge base formed on the basis of the team model of fuzzy neural networks can be effectively used to identify phishing sites on the Internet.

https://helixscientific.pub/index.php/home/article/download/237/190

Definition of Phishing Sites Based on the Team Model of Fuzzy Neural Networks

Multi–layer spam detection systems frequently used in many SMTP servers often suffer from a lack of mutual communication between individual layers. The paper presents the construction of a feedback interconnection between two significant layers, namely Message contents check and Greylisting. The verification in a real SMTP server is performed, demonstrating considerable improvement of spam detection efficiency comparing the previous period with missing interconnection, while for a short testing period. Despite the limited generalizability of the result, it suggests the easy way how spam detection can be improved.

Detection Efficiency Improvement in Multi–component Anti-spam Systems

This paper describes the technology of neural network application to solve the problem of information security incidents forecasting. We describe the general problem of analyzing and predicting time series in a graphical and mathematical setting. To solve this problem, it is proposed to use a neural network model. To solve the task of forecasting a time series of information security incidents, data are generated and described on the basis of which the neural network is trained. We offer a neural network structure, train the neural network, estimate it's adequacy and forecasting ability. We show the possibility of effective use of a neural network model as a part of an intelligent forecasting system.

Neural Network Model for Information Security Incident Forecasting

In this paper we solve the problem of neural network technology development for e-mail messages classification. We analyze basic methods of spam filtering such as a sender IP-address analysis, spam messages repeats detection and the Bayesian filtering according to words. We offer the neural network technology for solving this problem because the neural networks are universal approximators and effective in addressing the problems of classification. Also, we offer the scheme of this technology for e-mail messages “spam”/“not spam” classification. The creation of effective neural network model of spam filtering is performed within the databases knowledge discovery technology. For this training set is formed, the neural network model is trained, its value and classifying ability are estimated. The experimental studies have shown that a developed artificial neural network model is adequate and it can be effectively used for the e-mail messages classification. Thus, in this paper we have shown the possibility of the effective neural network model use for the e-mail messages filtration and have shown a scheme of artificial neural network model use as a part of the e-mail spam filtering intellectual system.

Lilia Yu. Emaletdinova

Papers

Neural Network Model for Information Security Incident Forecasting

Neural Network Spam Filtering Technology