• Title/Summary/Keyword: Fruad Detection

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Fraud detection support vector machines with a functional predictor: application to defective wafer detection problem (불량 웨이퍼 탐지를 위한 함수형 부정 탐지 지지 벡터기계)

  • Park, Minhyoung;Shin, Seung Jun
    • The Korean Journal of Applied Statistics
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    • v.35 no.5
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    • pp.593-601
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    • 2022
  • We call "fruad" the cases that are not frequently occurring but cause significant losses. Fraud detection is commonly encountered in various applications, including wafer production in the semiconductor industry. It is not trivial to directly extend the standard binary classification methods to the fraud detection context because the misclassification cost is much higher than the normal class. In this article, we propose the functional fraud detection support vector machine (F2DSVM) that extends the fraud detection support vector machine (FDSVM) to handle functional covariates. The proposed method seeks a classifier for a function predictor that achieves optimal performance while achieving the desired sensitivity level. F2DSVM, like the conventional SVM, has piece-wise linear solution paths, allowing us to develop an efficient algorithm to recover entire solution paths, resulting in significantly improved computational efficiency. Finally, we apply the proposed F2DSVM to the defective wafer detection problem and assess its potential applicability.

A Study on the Detection Model of Illegal Access to Large-scale Service Networks using Netflow (Netflow를 활용한 대규모 서비스망 불법 접속 추적 모델 연구)

  • Lee, Taek-Hyun;Park, WonHyung;Kook, Kwang-Ho
    • Convergence Security Journal
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    • v.21 no.2
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    • pp.11-18
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    • 2021
  • To protect tangible and intangible assets, most of the companies are conducting information protection monitoring by using various security equipment in the IT service network. As the security equipment that needs to be protected increases in the process of upgrading and expanding the service network, it is difficult to monitor the possible exposure to the attack for the entire service network. As a countermeasure to this, various studies have been conducted to detect external attacks and illegal communication of equipment, but studies on effective monitoring of the open service ports and construction of illegal communication monitoring system for large-scale service networks are insufficient. In this study, we propose a framework that can monitor information leakage and illegal communication attempts in a wide range of service networks without large-scale investment by analyzing 'Netflow statistical information' of backbone network equipment, which is the gateway to the entire data flow of the IT service network. By using machine learning algorithms to the Netfllow data, we could obtain the high classification accuracy of 94% in identifying whether the Telnet service port of operating equipment is open or not, and we could track the illegal communication of the damaged equipment by using the illegal communication history of the damaged equipment.