B‑VAE: a new dataset balancing approach using batched Variational AutoEncoders to enhance network intrusion detection
學年 111
學期 2
出版（發表）日期 2023-03-22
作品名稱 B‑VAE: a new dataset balancing approach using batched Variational AutoEncoders to enhance network intrusion detection
作品名稱（其他語言）
著者 Chuang, Po-jen; Huang, Pang‑yu
單位
出版者
著錄名稱、卷期、頁數 The Journal of Supercomputing 79(12), 13262-13286
摘要 Data imbalance in network intrusion detection datasets tends to incur underfitting or deviation in classifier training. This investigation applies Batched Variational AutoEncoders (B-VAE) to generate a desirable data generation model which can balance intrusion detection datasets to enhance the detection practice. To improve insufficient VAE decoder training in the VAE approach, we apply B-VAE to train one decoder for each piece of data by a batched duplicated data and form multiple batched VAEs to provide sufficient decoder training. The unique practice of B-VAE makes the generated data all similar to but different from the original data, to secure desirable data balance for better classifier training and classification results. Experimental evaluation conducted to compare the performance of related balancing approaches shows that our B-VAE outperforms others in that it is able to maintain the same classification accuracy (in terms of F1-scores) regardless of any Imbalance Ratio (IR) change. Specifically, B-VAE manages to solve the problem of insufficient decoder training in existing approaches and so to enhance the intrusion detection performance—mainly because it can secure balanced data generation to lift the classification accuracy due to sufficient decoder training and utilization of exact features.
關鍵字 Network intrusion detection；Balanced datasets；Variational AutoEncoders；Balancing approaches；Performance evaluation
語言 en
ISSN 1573-0484
期刊性質 國外
收錄於 SCI
產學合作
通訊作者
審稿制度 是
國別 USA
公開徵稿
出版型式 ,電子版,紙本
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機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/124006 )

SDGS 優質教育,產業創新與基礎設施