Exploring network security threats through text mining techniques: a comprehensive analysis

Tri Wahyuningsih, Irwan Sembiring, Adi Setiawan, Iwan Setyawan

Abstract


In response to the escalating cybersecurity threats, this research focuses on leveraging text mining techniques to analyze network security data effectively. The study utilizes user-generated reports detailing attacks on server networks. Employing clustering algorithms, these reports are grouped based on threat levels. Additionally, a classification algorithm discerns whether network activities pose security risks. The research achieves a noteworthy 93% accuracy in text classification, showcasing the efficacy of these techniques. The novelty lies in classifying security threat report logs according to their threat levels. Prioritizing high-risk threats, this approach aids network management in strategic focus. By enabling swift identification and categorization of network security threats, this research equips organizations to take prompt, targeted actions, enhancing overall network security.

Keywords


Classification; Clustering; Network risk analysis; Network security; Text mining

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DOI: https://doi.org/10.11591/csit.v4i3.p258-267

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Computer Science and Information Technologies
ISSN: 2722-323X, e-ISSN: 2722-3221
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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