A review on graph-based approaches for network security
monitoring and botnet detection

J 2024

A review on graph-based approaches for network security monitoring and botnet detection

LAGRAA, Sofiane; Martin HUSÁK; Hamida SEBA; Satyanarayana VUPPALA; Radu STATE et. al.

Základní údaje

Originální název

A review on graph-based approaches for network security monitoring and botnet detection

Autoři

LAGRAA, Sofiane; Martin HUSÁK (203 Česká republika, garant, domácí); Hamida SEBA; Satyanarayana VUPPALA; Radu STATE a Moussa OUEDRAOGO

Vydání

International Journal of Information Security, Springer, 2024, 1615-5262

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10200 1.2 Computer and information sciences

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 2.400 v roce 2023

Kód RIV

RIV/00216224:14610/24:00135191

Organizační jednotka

Ústav výpočetní techniky

DOI

http://dx.doi.org/10.1007/s10207-023-00742-7

UT WoS

001062032500001

EID Scopus

2-s2.0-85169463507

Klíčová slova anglicky

Graph theory;Machine learning;Network security;Botnet detection;Monitoring;Cybersecurity

Štítky

J-Q2, rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 24. 3. 2025 15:25, Mgr. Eva Špillingová

Anotace

V originále

This survey paper provides a comprehensive overview of recent research and development in network security that uses graphs and graph-based data representation and analytics. The paper focuses on the graph-based representation of network traffic records and the application of graph-based analytics in intrusion detection and botnet detection. The paper aims to answer several questions related to graph-based approaches in network security, including the types of graphs used to represent network security data, the approaches used to analyze such graphs, the metrics used for detection and monitoring, and the reproducibility of existing works. The paper presents a survey of graph models used to represent, store, and visualize network security data, a survey of the algorithms and approaches used to analyze such data, and an enumeration of the most important graph features used for network security analytics for monitoring and botnet detection. The paper also discusses the challenges and limitations of using graph-based approaches in network security and identifies potential future research directions. Overall, this survey paper provides a valuable resource for researchers and practitioners in the field of network security who are interested in using graph-based approaches for analyzing and detecting malicious activities in networks.

Návaznosti

EF16_019/0000822, projekt VaV

Název: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur

Citovat

LAGRAA, Sofiane; Martin HUSÁK; Hamida SEBA; Satyanarayana VUPPALA; Radu STATE a Moussa OUEDRAOGO. A review on graph-based approaches for network security monitoring and botnet detection. International Journal of Information Security. Springer, 2024, roč. 23, č. 1, s. 119-140. ISSN 1615-5262. Dostupné z: https://dx.doi.org/10.1007/s10207-023-00742-7.

@article{2309857,
   author = {Lagraa, Sofiane and Husák, Martin and Seba, Hamida and Vuppala, Satyanarayana and State, Radu and Ouedraogo, Moussa},
   article_number = {1},
   doi = {http://dx.doi.org/10.1007/s10207-023-00742-7},
   keywords = {Graph theory;Machine learning;Network security;Botnet detection;Monitoring;Cybersecurity},
   language = {eng},
   issn = {1615-5262},
   journal = {International Journal of Information Security},
   title = {A review on graph-based approaches for network security monitoring and botnet detection},
   url = {https://link.springer.com/article/10.1007/s10207-023-00742-7},
   volume = {23},
   year = {2024}
}

TY  - JOUR
ID  - 2309857
AU  - Lagraa, Sofiane - Husák, Martin - Seba, Hamida - Vuppala, Satyanarayana - State, Radu - Ouedraogo, Moussa
PY  - 2024
TI  - A review on graph-based approaches for network security monitoring and botnet detection
JF  - International Journal of Information Security
VL  - 23
IS  - 1
SP  - 119-140
EP  - 119-140
PB  - Springer
SN  - 16155262
KW  - Graph theory;Machine learning;Network security;Botnet detection;Monitoring;Cybersecurity
UR  - https://link.springer.com/article/10.1007/s10207-023-00742-7
N2  - This survey paper provides a comprehensive overview of recent research and development in network security that uses graphs and graph-based data representation and analytics. The paper focuses on the graph-based representation of network traffic records and the application of graph-based analytics in intrusion detection and botnet detection. The paper aims to answer several questions related to graph-based approaches in network security, including the types of graphs used to represent network security data, the approaches used to analyze such graphs, the metrics used for detection and monitoring, and the reproducibility of existing works. The paper presents a survey of graph models used to represent, store, and visualize network security data, a survey of the algorithms and approaches used to analyze such data, and an enumeration of the most important graph features used for network security analytics for monitoring and botnet detection. The paper also discusses the challenges and limitations of using graph-based approaches in network security and identifies potential future research directions. Overall, this survey paper provides a valuable resource for researchers and practitioners in the field of network security who are interested in using graph-based approaches for analyzing and detecting malicious activities in networks.
ER  -

LAGRAA, Sofiane; Martin HUSÁK; Hamida SEBA; Satyanarayana VUPPALA; Radu STATE a Moussa OUEDRAOGO. A review on graph-based approaches for network security monitoring and botnet detection. \textit{International Journal of Information Security}. Springer, 2024, roč.~23, č.~1, s.~119-140. ISSN~1615-5262. Dostupné z: https://dx.doi.org/10.1007/s10207-023-00742-7.

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