The Journal of Information Systems (한국정보시스템학회지:정보시스템연구)
- Volume 15 Issue 4
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- Pages.211-224
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- 2006
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- 1229-8476(pISSN)
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- 2733-8770(eISSN)
Intelligent Intrusion Detection Systems Using the Asymmetric costs of Errors in Data Mining
데이터 마이닝의 비대칭 오류비용을 이용한 지능형 침입탐지시스템 개발
Abstract
This study investigates the application of data mining techniques such as artificial neural networks, rough sets, and induction teaming to the intrusion detection systems. To maximize the effectiveness of data mining for intrusion detection systems, we introduced the asymmetric costs with false positive errors and false negative errors. And we present a method for intrusion detection systems to utilize the asymmetric costs of errors in data mining. The results of our empirical experiment show our intrusion detection model provides high accuracy in intrusion detection. In addition the approach using the asymmetric costs of errors in rough sets and neural networks is effective according to the change of threshold value. We found the threshold has most important role of intrusion detection model for decreasing the costs, which result from false negative errors.