Dong-Wook Kim1, Gun-Yoon Shin1, Myung-Mook Han2,*
Computer Systems Science and Engineering, Vol.46, No.1, pp. 153-164, 2023, DOI:10.32604/csse.2023.034589
Abstract Unlike external attacks, insider threats arise from legitimate users who belong to the organization. These individuals may be a potential threat for hostile behavior depending on their motives. For insider detection, many intrusion detection systems learn and prevent known scenarios, but because malicious behavior has similar patterns to normal behavior, in reality, these systems can be evaded. Furthermore, because insider threats share a feature space similar to normal behavior, identifying them by detecting anomalies has limitations. This study proposes an improved anomaly detection methodology for insider threats that occur in cybersecurity in which a discrete wavelet transformation technique is applied… More >
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