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http://dx.doi.org/10.33778/kcsa.2019.19.4.181

A Study on the Performance Improvement of Anomaly-Based IDS Through the Improvement of Training Data  

Moon, Sang Tae (국방대학교 국방과학학과)
Lee, Soo Jin (국방대학교 국방과학학과)
Publication Information
Convergence Security Journal / v.19, no.4, 2019 , pp. 181-188 More about this Journal
Abstract
Recently, attempts to apply artificial intelligence technology to create the normal profile in Anomaly-based intrusion detection systems have been made actively. But existing studies that proposed the application of artificial intelligence technology mostly focus on improving the structure of artificial neural networks and finding optimal hyper-parameter values, and fail to address various problems that may arise from the misconfiguration of learning data. In this paper, we identify the main problems that may arise due to the misconfiguration of learning data through experiment. And we also propose a novel approach that can address such problems and improve the detection performance through reconstruction of learning data.
Keywords
Anomaly based Intrusion Detection System; Artificial Neural Network; Machine learning;
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Times Cited By KSCI : 1  (Citation Analysis)
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