Title Modelling and assessment of asynchronous evidence-based network flows for cyber-attack detection
Authors Krinickij, Virgilijus ; Bukauskas, Linas
DOI 10.22364/bjmc.2026.14.2.06
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Is Part of Baltic journal of modern computing.. Riga : University of Latvia. 2026, vol. 14, no. 2, p. 376-401.. ISSN 2255-8942. eISSN 2255-8950
Keywords [eng] network traffic analysis ; dynamic time warping ; incident detection ; asynchronous evidence assessment ; synthetic attack modelling ; cybersecurity
Abstract [eng] In cybersecurity, analysing network traffic is critical for identifying potential threats and mitigating incidents. Traditional approaches to network traffic analysis often rely on synchronous methods that may not fully capture the dynamic nature of network behaviour. This paper presents an approach for asynchronous evidence-based network traffic assessment, experimenting on synthetic cyber-attack templates and large network flow datasets available online. Our approach leverages asynchronous data collection to capture network traffic at varying time intervals, enabling the identification of incidents across different time frames and locations by processing and aligning the data. By combining asynchronous data collection with evidence-based assessment, our approach enables cybersecurity analysts to gain deeper insight into network traffic dynamics, enhance threat detection capabilities, and improve incident response effectiveness. We demonstrate the effectiveness of our approach through experimental evaluations using synthetic cyber-attack templates in a virtual environment, while capturing network flows. In summary, our research advances the field of network traffic analysis by presenting an approach that addresses the shortcomings of conventional synchronous techniques and lays the groundwork for more resilient, adaptive cybersecurity solutions.
Published Riga : University of Latvia
Type Journal article
Language English
Publication date 2026
CC license CC license description