A Cost-Efficient Software Based Router and Traffic Generator for Simulation and Testing of IP Network

Su, Jun; Przystupa, Krzysztof; Beshley, Mykola; Кочан, Орест; Beshley, Halyna; Klymash, Mykhailo; Wang, Jinfei; Pieniak, Daniel

doi:10.3390/electronics9010040

Cited by 33 publications

(22 citation statements)

References 54 publications

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“…The K-Means [48][49][50] algorithm uses distance as the classification criterion. The smaller the distance between two samples, the more similar they are.…”

Section: Obtaining the Optimal Anchor Boxesmentioning

confidence: 99%

Examination of Abnormal Behavior Detection Based on Improved YOLOv3

et al. 2021

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Examination is a way to select talents, and a perfect invigilation strategy can improve the fairness of the examination. To realize the automatic detection of abnormal behavior in the examination room, the method based on the improved YOLOv3 (The third version of the You Only Look Once algorithm) algorithm is proposed. The YOLOv3 algorithm is improved by using the K-Means algorithm, GIoUloss, focal loss, and Darknet32. In addition, the frame-alternate dual-thread method is used to optimize the detection process. The research results show that the improved YOLOv3 algorithm can improve both the detection accuracy and detection speed. The frame-alternate dual-thread method can greatly increase the detection speed. The mean Average Precision (mAP) of the improved YOLOv3 algorithm on the test set reached 88.53%, and the detection speed reached 42 Frames Per Second (FPS) in the frame-alternate dual-thread detection method. The research results provide a certain reference for automated invigilation.

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“…The K-Means [48][49][50] algorithm uses distance as the classification criterion. The smaller the distance between two samples, the more similar they are.…”

Section: Obtaining the Optimal Anchor Boxesmentioning

confidence: 99%

Examination of Abnormal Behavior Detection Based on Improved YOLOv3

et al. 2021

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“…The scheme of the experimental stand consists of the following basic elements: end devices (IoT camera, laptop, and computer) that generate legitimate traffic; the generator of non-legitimate traffic (network attack), which is a conventional computer on the corporate network [84]; network devices on the corporate network (switch and router), and the object of attack, which is a standard web server on the Internet and installed before the router software DPI system.…”

Section: Test Bed For Network Traffic Analysis and Anomaly Detectionmentioning

confidence: 99%

“…The network traffic analysis and anomaly detection scheme using the SolarWinds DPI system is depicted in Figure 20 and the scheme using the proposed DPI system is depicted in Figure 21. (network attack), which is a conventional computer on the corporate network [84]; network devices on the corporate network (switch and router), and the object of attack, which is a standard web server on the Internet and installed before the router software DPI system. The network traffic analysis and anomaly detection scheme using the SolarWinds DPI system is depicted in Figure 20 and the scheme using the proposed DPI system is depicted in Figure 21.…”

Section: Test Bed For Network Traffic Analysis and Anomaly Detectionmentioning

confidence: 99%

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A Software Deep Packet Inspection System for Network Traffic Analysis and Anomaly Detection

Song

Beshley

Przystupa

et al. 2020

Sensors

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In this paper, to solve the problem of detecting network anomalies, a method of forming a set of informative features formalizing the normal and anomalous behavior of the system on the basis of evaluating the Hurst (H) parameter of the network traffic has been proposed. Criteria to detect and prevent various types of network anomalies using the Three Sigma Rule and Hurst parameter have been defined. A rescaled range (RS) method to evaluate the Hurst parameter has been chosen. The practical value of the proposed method is conditioned by a set of the following factors: low time spent on calculations, short time required for monitoring, the possibility of self-training, as well as the possibility of observing a wide range of traffic types. For new DPI (Deep Packet Inspection) system implementation, algorithms for analyzing and captured traffic with protocol detection and determining statistical load parameters have been developed. In addition, algorithms that are responsible for flow regulation to ensure the QoS (Quality of Services) based on the conducted static analysis of flows and the proposed method of detection of anomalies using the parameter Hurst have been developed. We compared the proposed software DPI system with the existing SolarWinds Deep Packet Inspection for the possibility of network traffic anomaly detection and prevention. The created software components of the proposed DPI system increase the efficiency of using standard intrusion detection and prevention systems by identifying and taking into account new non-standard factors and dependencies. The use of the developed system in the IoT communication infrastructure will increase the level of information security and significantly reduce the risks of its loss.

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“…In this simulation program, seven types of electronic services that can be transmitted by the network are selected: VoIP (voice over Internet Protocol), data transfer, interactive data, IPTV Internet Protocol Television, service packets, videoconferencing, video on demand [40].…”

Section:  Packets Generation By Customer Devicesmentioning

confidence: 99%

Customer-Oriented Quality of Service Management Method for the Future Intent-Based Networking

et al. 2020

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The rapid development and spread of communication technologies is now becoming a global information revolution. Customers have a need for communication services, which could be flexibly configured in accordance with their Quality of Experience (QoE) requirements. Realizing the close connection between customer experience and profitability, the service provider has been placing more and more attention on customer experience and QoE. The traditional quality of service management method based on SLA (Service Level Agreement) is not sufficient as a means to provide QoE-related contracts between service providers and customers. The current SLA method is mostly limited and focused on technical aspects of QoS (Quality of Service). Furthermore, they do not follow on the network the principles and semantic approach to the QoS specification for a communication service using QoE parameters. In this paper, we propose a customer-oriented quality of service management method for future IBN (Intent-Based Networking). It is based on a new QoE metric on a scale from 1 to 5, which allows one to take into account the commercial value of e-services for customers. Based on this approach, the network configuration and functionality of network equipment automatically changes depending on customer requirements. To implement the new method of service quality management, an algorithm for routing data packets in the network was developed, taking into account the current load of the forecast path. The algorithm of billing system functioning in conditions of customer-oriented quality management in telecommunication networks has been created. To investigate the effectiveness of the proposed method of service quality management with the traditional SLA method, we developed a simulation network model with the implementation of two approaches. By conducting a simulation, it was determined that the proposed method gives an average gain of 2–5 times for the criterion of the number of customers who require high quality of experience of the service.

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A Cost-Efficient Software Based Router and Traffic Generator for Simulation and Testing of IP Network

Cited by 33 publications

References 54 publications

Examination of Abnormal Behavior Detection Based on Improved YOLOv3

Examination of Abnormal Behavior Detection Based on Improved YOLOv3

A Software Deep Packet Inspection System for Network Traffic Analysis and Anomaly Detection

Customer-Oriented Quality of Service Management Method for the Future Intent-Based Networking

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