• The Transactions of the Korea Information Processing Society
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• v.7 no.8
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• pp.2411-2420
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• 2000

Far detecting variaus camputer intrusians effectively, many researches have develaped the misuse based intrusian detectian systems. Recently, warks related ta anamaly detectian, which have impraved the drawback .of misuse detectian technique, have been under focus. In this paper, a new clustering algarithm based an support constraint far generating user's narmal activity patterns in the anamaly detectian can praposed. It can grant a user's activity .observed recently ta mare weight than that .observed in the past. In order that a user's anamaly can be analyzed in variaus angles, a user's activity is classified by many measures, and far each .of them user's narmal patterns can be generated. by using the proposed algarithm. As a result, using generated narmal patterns, user's anamaly can be detected easily and effectively.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.12 no.5
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• pp.63-73
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• 2002

Recently, intrusions into a computer have been increased rapidly and also various intrusion methods have been developed. As a result. many researches have been performed to detect the activities of intruders effectively In this paper, a new association mining algorithm for anomaly network intrusion detection is proposed. For this purpose, the proposed algorithm is composed of two different phases: intra-packet association and inter-packet association. The performance of the proposed anomaly detection system is evaluated based on several experiment according to various system parameters in order to identify their practical ranges for maximizing its detection rate. As a result, an anomaly can be detected effectively.

• 한국IT서비스학회:학술대회논문집
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• 2009.11a
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• pp.524-527
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• 2009

최근 감시, 상황판단, 정보전달에 있어서 비디오 영상의 사용이 점점 증가하고 있다. 그러나 비디오 영상에 나타나는 객체들의 비정상행위를 탐지하는 것은 사용자에게 의존한다. 따라서 사용자가 비정상 행위를 놓치기 쉽고, 상황에 대한 대처가 늦어진다는 문제가 발생한다. 이러한 점을 개선하기 위해 실시간 영상 마이닝 기법을 이용한 비정상행위 탐지법이 연구되었으나, 제약 조건이 심하고, 불필요하게 추적되는 데이터가 많아 효율이 떨어진다는 단점이 있다. 본 논문에서는 이러한 단점을 개선하여 3차원 환경에서의 객체의 추적에 대한 정확도를 높이고 일반적인 상황에서도 적용이 가능한 비디오 마이닝을 이용한 비정상 행위 탐지 기법을 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.04b
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• pp.845-848
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• 2002

네트워크 기반 침입탐지시스템은 연속적으로 발생하는 패킷의 무손실 축소와 행위패턴을 정확히 모델링 할 수 있는 Event 의 생성이 전체성능을 결정하는 중요한 요인이 된다. 또한 공격이나 비정상 행위의 판별을 위해서는 효과적인 탐지모델의 구축이 필요하다. 본 논문은 네트워크기반에서 패킷을 분석해 비정상행위 수준을 관리자에게 보고하는 시스템의 설계에 관한 논문이다. 속성을 생성하고 선택하는 방법으로는 전문가의 경험을 바탕으로 결정하였고, 탐지모델구축은 COBWEB 클러스터링 기법을 사용하였다. 비정상행위 수준을 결정하기 위해 트레이닝 셋에 정상과 비정상의 비율을 두어 클러스터링 이후 탐지모드에서 새로운 온라인 Event 의 비정상 수준을 결정할 수 있게 하였다

• Journal of the Korea Institute of Information Security & Cryptology
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• v.13 no.2
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• pp.99-106
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• 2003

Intrusions pose a serious security risk in a network environment. For detecting the intrusion effectively, many researches have developed data mining framework for constructing intrusion detection modules. Traditional anomaly detection techniques focus on detecting anomalies in new data after training on normal data. To detect anomalous behavior, Precise normal Pattern is necessary. This training data is typically expensive to produce. For this, the understanding of the characteristics of data on network is inevitable. In this paper, we propose to use clustering and association rules as the basis for guiding anomaly detection. For applying entropy to filter noisy data, we present a technique for detecting anomalies without training on normal data. We present dynamic transaction for generating more effectively detection patterns.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.11b
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• pp.869-872
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• 2002

네트워크 기반 침입탐지시스템은 연속적으로 발생하는 패킷의 무손실 축소와, 패킷으로 정상 또는 비정상 행위패턴을 정확히 모델링한 모델 생성이 전체성능을 판단하는 중요한 요소가 된다. 네트워크 기반 비정상행위 판정 침입탐지시스템에서는 이러한 탐지모델 구축을 위해 주로 감독학습 알고리즘을 사용한다. 본 논문은 탐지모델 구축에 사용하는 감독 학습 방식이 가지는 문제점을 지적하고, 그에 대한 대안으로 비감독 학습방식의 학습알고리즘을 제안한다. 감독 학습을 사용하여 탐지모델을 구축하기 위해서는 정상행위의 패킷을 취합해야 하는 사전 부담이 있는 반면에 비감독 학습을 사용하게 되면 이러한 사전작업 없이 탐지모델을 구축할 수 있다. 본 논문에서는 비감독학습 알고리즘을 비교 분석하기 위해서 COBWEB, k-means, Autoclass 알고리즘을 사용했으며, 성능을 평가하기 위해서 비정상행위도(Abnormal Behavior Level)를 계산하여 에러율을 구하였다.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2002.06a
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• pp.81-82
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• 2002

• Journal of Korea Multimedia Society
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• v.9 no.8
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• pp.1030-1036
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• 2006

The rapid proliferation of wireless networks and mobile computing applications has changed the landscape of network security. Anomaly detection is a pattern recognition task whose goal is to report the occurrence of abnormal or unknown behavior in a given system being monitored. This paper presents an efficient rough set based anomaly detection method that can effectively identify a group of especially harmful internal masqueraders in cellular mobile networks. Our scheme uses the trace data of wireless application layer by a user as feature value. Based on the feature values, the use pattern of a mobile's user can be captured by rough sets, and the abnormal behavior of the mobile can be also detected effectively by applying a roughness membership function considering weighted feature values. The performance of our scheme is evaluated by a simulation. Simulation results demonstrate that the anomalies are well detected by the method that assigns different weighted values to feature attributes depending on importance.

• Journal of KIISE
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• v.44 no.4
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• pp.417-422
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• 2017

Anomaly detection is the identification of data that do not conform to a normal pattern or behavior model in a dataset. It can be utilized for detecting errors among data generated by devices or user behavior change in a social network data set. In this study, we proposed a new approach using rank correlation coefficient to efficiently detect abnormal data in devices of a building. With the increased push for energy conservation, many energy efficiency solutions have been proposed over the years. HVAC (Heating, Ventilating and Air Conditioning) system monitors and manages thousands of sensors such as thermostats, air conditioners, and lighting in large buildings. Currently, operators use the building's HVAC system for controlling efficient energy consumption. By using the proposed approach, it is possible to observe changes of ranking relationship between the devices in HVAC system and identify abnormal behavior in social network.

• The Transactions of the Korea Information Processing Society
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• v.7 no.11
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• pp.3445-3461
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• 2000

Although many different intrusion detectionsystems have been developed there have not been enough researches on the methodology for evaluating these intrusion delection systems. With this understanding,in this paper we present a methodology for evaluating infrusion detection systems from the view point of performance and robustness, both of which are considered the most important criteria Current research on evaluating the performance f intrusion detection systems mostly foduson the in issuse detection but not on the anormaly detection. Regarding evalieting robustness it is not easy to apply off -line methodologies and methods for testing robustness hae not been proposed in on -line methodolomes, In this paper we provide an systematic way of classifyin and generating anomalies and using this reult, present an methodology for evaluating the pertormance of intrusion detection systems in detecting anomaalies ans well as misuses . Moreover, ww study the factors that can damage the robustness of intrusion detection systems and suggest an methodology for assessing the robustness of intrusion detection systems.