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Botnet Detection Through Periodic Patterns in Command-and-Control Network Traffic

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F63839172%3A_____%2F25%3A10133872" target="_blank" >RIV/63839172:_____/25:10133872 - isvavai.cz</a>

  • Alternative codes found

    RIV/68407700:21240/25:00386912

  • Result on the web

    <a href="http://dx.doi.org/10.23919/CNSM67658.2025.11297465" target="_blank" >http://dx.doi.org/10.23919/CNSM67658.2025.11297465</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.23919/CNSM67658.2025.11297465" target="_blank" >10.23919/CNSM67658.2025.11297465</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Botnet Detection Through Periodic Patterns in Command-and-Control Network Traffic

  • Original language description

    Detecting botnet Command-and-Control (C&amp;C) communication in encrypted network traffic is a persistent challenge in cybersecurity, particularly in environments without endpoint visibility. We present a novel approach for botnet detection based on the inherent periodic communication patterns of C&amp;C channels. Leveraging the Lomb-Scargle periodogram, we identify periodic behaviour in multiflow time series and extract periodic-based features for classification using machine learning. To address limitations in existing datasets, we introduce CESNET-CC25, a comprehensive and publicly available dataset comprising real-world botnet C&amp;C traffic and benign traffic collected from an ISP backbone and controlled laboratory settings. Our method achieves high precision across both the widely used CTU-13 dataset and CESNET-CC25, with significant improvements in recall on long-duration captures. The results demonstrate that periodicity is a reliable indicator of C&amp;C behaviour, even in modern, encrypted network environments, and that CESNET-CC25 provides a realistic benchmark for future botnet detection research.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    Result was created during the realization of more than one project. More information in the Projects tab.

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2025

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Proceedings of the 2025 21th International Conference on Network and Service Management CNSM 2025

  • ISBN

    978-3-903176-75-1

  • ISSN

    2165-963X

  • e-ISSN

  • Number of pages

    6

  • Pages from-to

  • Publisher name

    IEEE

  • Place of publication

    Bologna, Italy

  • Event location

    Bologna, Italy

  • Event date

    Oct 27, 2025

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article