Knowledge graph reasoning for cyber attack detection

Autor: Ezekia Gilliard, Jinshuo Liu, Ahmed Abubakar Aliyu
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: IET Communications, Vol 18, Iss 4, Pp 297-308 (2024)
Druh dokumentu: article
ISSN: 1751-8636
1751-8628
52673405
DOI: 10.1049/cmu2.12736
Popis: Abstract In today's digital landscape, cybercriminals are constantly evolving their tactics, making it challenging for traditional cybersecurity methods to keep up. To address this issue, this study explores the potential of knowledge graph reasoning as a more adaptable and sophisticated approach to identify and counter network attacks. By leveraging graph structures imbued with human‐like thinking, this method enhances the resilience of cybersecurity systems. The study focuses on three critical aspects: data preparation, semantic foundations, and knowledge graph inference techniques. Through an in‐depth analysis of these components, the research aims to reveal how knowledge graph reasoning can improve cyberattack detection and enhance the overall efficacy of cybersecurity measures, including intrusion detection systems. The proposed approach has undergone extensive experimentation to validate its effectiveness compared to existing methods. The results of the experiment have shown a remarkable advancement in accuracy, speed, and recall for recognition, surpassing current methods. This achievement is a notable contribution in the realm of managing big data in cybersecurity. The study establishes a foundation for the automation of network attack detection, ultimately enhancing overall network security.
Databáze: Directory of Open Access Journals
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