Work

Improving Network Attack Classification on Imbalanced Real-world Intrusion Incident Datasets

Year: 2025

Type: conference-paper

Abstract: In real-world datasets, the volume of collected data varies according to the characteristics of each attack type, resulting in class-imbalanced datasets. This class imbalance impacts attack type classifier performance by causing mispredictions of minority attack types, leading to unreliable detection of less common, yet more harmful, intrusions. To address this issue, we propose a two-step pair selection strategy to enhance contrastive learning (CL) in the realistic network traffic environment. We evaluate the proposed solution using datasets collected by the Korea Internet Security Agency (KISA) based on real-world cybersecurity incidents.

Source: International Conference on Mobile Systems, Applications, and Services

DOI: 10.1145/3711875.3734549

Authors: Miru Kim, Mugon Joe, Minhae Kwon

Institution: Soongsil University

Language: English

Topic: Network Security and Intrusion Detection

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funders: National Research Foundation, National Research Foundation of Korea, Ministry of Science and ICT, South Korea, Institute for Information and Communications Technology Promotion

Awards: G1701925455, G5216695908, G8134130306