iconicon Uproszczony widok
 

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

cris.lastimport.scopus2024-09-19T01:30:55Z
dc.abstract.enIn 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.
dc.affiliationTransportu i Informatyki
dc.contributor.authorWenguang Song
dc.contributor.authorMykola Beshley
dc.contributor.authorKrzysztof Przystupa
dc.contributor.authorHalyna Beshley
dc.contributor.authorOrest Kochan
dc.contributor.authorAndrii Pryslupskyi
dc.contributor.authorDaniel Pieniak
dc.contributor.authorJun Su
dc.date.accessioned2024-07-05T09:13:12Z
dc.date.available2024-07-05T09:13:12Z
dc.date.issued2020
dc.identifier.doi10.3390/s20061637
dc.identifier.issn1424-8220
dc.identifier.urihttps://repo.akademiawsei.eu/handle/item/374
dc.languageen
dc.pbn.affiliationinformation and communication technology
dc.relation.ispartofSensors
dc.rightsCC-BY
dc.subject.enIoT
dc.subject.enWSN
dc.subject.ennetwork anomaly
dc.subject.enHurst parameter
dc.subject.enDPI
dc.subject.enintrusion detection
dc.titleA Software Deep Packet Inspection System for Network Traffic Analysis and Anomaly Detection
dc.typeReviewArticle
dspace.entity.typePublication
oaire.citation.issue6
oaire.citation.volume20