• Proceedings of the Korea Information Processing Society Conference
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• 2003.11c
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• pp.1913-1916
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• 2003

오용행위와 비정상행위 그리고 알려지지 않은 공격을 탐지하기 위해 필요한 규칙들을 추출하는 방법이 계속 연구되고 있다. 기존의 네트워크 공격에 대한 침입탐지시스템의 탐지 패턴은 전문가의 수작업에 의해 생성되어 왔고, 수정이 필요할 경우 수작업을 필요로 했다. 그러나 네트워크 공격은 매시간 다양화되고 변형되기 때문에 적절한 대응이 필요하다. 본 논문에서는 이같은 문제를 결정트리를 사용하여 네트워크 패킷 내에서 공격형태를 패턴화하여 자동으로 탐지 패턴을 추출하는 방법을 제안한다.

• Journal of the Korea Society of Computer and Information
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• v.19 no.1
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• pp.85-94
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• 2014

This paper studied the detection technique using file DNA-based behavior pattern analysis in order to minimize damage to user system by malicious programs before signature or security patch is released. The file DNA-based detection technique was applied to defend against zero day attack and to minimize false detection, by remedying weaknesses of the conventional network-based packet detection technique and process-based detection technique. For the file DNA-based detection technique, abnormal behaviors of malware were splitted into network-related behaviors and process-related behaviors. This technique was employed to check and block crucial behaviors of process and network behaviors operating in user system, according to the fixed conditions, to analyze the similarity of behavior patterns of malware, based on the file DNA which process behaviors and network behaviors are mixed, and to deal with it rapidly through hazard warning and cut-off.

• 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.11
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• pp.1178-1193
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• 2017

It is very important to understand system behaviors in collective pattern for each knowledge domain. However, there are structural limitations to represent collective behaviors because of the size of system components and the complexity of their interactions, causing the state explosion problem. Further composition with other systems is mostly impractical because of exponential growth of their size and complexity. This paper presents a practical method to model the collective behaviors, based on a new concept of domain engineering: behavior ontology. Firstly, the ontology defines each collective behavior of a system from active ontology. Secondly, the behaviors are formed in a quantifiably abstract lattice, called common regular expression. Thirdly, a lattice can be composed with other lattices based on quantifiably common elements. The method can be one of the most innovative approaches in representing system behaviors in collective pattern, as well as in minimization of system states to reduce system complexity. For implementation, a prototype tool, called PRISM, has been developed on ADOxx Meta-Modelling Platform.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.6-8
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• 2022

Web log analysis is one of the essential procedures for service improvement. ScienceON is a representative information service that provides various S&T literature and information, and we analyze its logs for continuous improvement. This study aims to analyze ScienceON web logs recorded in May 2020 and May 2021, dividing them into humans and web crawlers and performing an in-depth analysis. First, only web logs corresponding to S (search), V (detail view), and D (download) types are extracted and normalized to 658,407 and 8,727,042 records for each period. Second, using the Python 'user_agents' library, the logs are classified into humans and web crawlers, and third, the session size was set to 60 seconds, and each session is analyzed. We found that web crawlers, unlike humans, show relatively long for the average behavior pattern per session, and the behavior patterns are mainly for V patterns. As the future, the service will be improved to quickly detect and respond to web crawlers and respond to the behavioral patterns of human users.

• Journal of the Korean Data and Information Science Society
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• v.20 no.2
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• pp.387-399
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• 2009

Mobile wireless networks continue to be plagued by theft of identify and intrusion. Both problems can be addressed in two different ways, either by misuse detection or anomaly-based detection. In this paper, we propose a dissimilarity-based anomaly detection method which can effectively identify abnormal behavior such as mobility patterns of mobile wireless networks. In the proposed algorithm, a normal profile is constructed from normal mobility patterns of mobile nodes in mobile wireless networks. From the constructed normal profile, a dissimilarity is computed by a weighted dissimilarity measure. If the value of the weighted dissimilarity measure is greater than the dissimilarity threshold that is a system parameter, an alert message is occurred. The performance of the proposed method is evaluated through a simulation. From the result of the simulation, we know that the proposed method is superior to the performance of other anomaly detection methods using dissimilarity measures.

• Proceedings of the Korean Society of Computer Information Conference
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• 2009.01a
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• pp.231-236
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• 2009

본 연구에서는 집이나 사무실과 같은 일상 공간에서 발생할 수 있는 연간의 일상생활행위 (ADL: Activities of Daily Living) 들을 인지하는 분산 모델을 제시한다. 사용자의 환경 정보, 위치 정보 및 행위 정보를 간단한 센서들이 부착된 가정용 기기들과 가구, 식기들을 통해 무선 센서 네트워크를 통해 수집하며 분석한다. 하지만 이와 같은 다양한 기기의 활용과 충분히 분석되어지지 않은 데이터들은 본 논문에서 제시하는 일상 환경에서 고차원의 ADL 모델을 구축하기 어렵게 한다. 그러나 ADL들이 생성하는 센서 데이터들과 센서 데이터들의 순서들은 어떤 행위가, 이루어지고 있는지 인지할 수 있도록 도와준다. 따라서 이 센서 데이터들의 순서를 특정 행위 패턴을 분석하는 데 활용하고, 이를 통해 분산 선형 시간 추론 알고리즘을 제안한다. 이 알고리즘은 센서 네트워크와 같은 소규모 시스템에서 행위를 인지하는 데 적절하다.

• 한국IT서비스학회:학술대회논문집
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• 2009.11a
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• pp.511-515
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• 2009

최근 정보기술의 발달과 함께 지속적으로 다양해지고 빨라지는 침입 방법에 대처하기 위해 정보를 보호하기 위한 새로운 방법이 요구되고 있는 실정이다. 이를 해결하기 위해 제안된 방법 중 하나가 네트워크 패킷 데이터에 대한 실시간 데이터 스트림 마이닝 알고리즘 기반의 비정상행위 탐지 기법이다. 이는 현재 발생하고 있는 패턴이 기존 패턴과 다를 경우 비정상행위로 간주되고 사용자에게 알려주는 방법으로, 지금까지 없었던 새로운 형태의 침입에도 대처할 수 있는 능동적인 방어법이라고 할 수 있다. 그러나 이 방법에서 네트워크 패킷 데이터 정보만을 통해 얻어낼 수 있는 정보에는 한계가 있다. 따라서, 본 논문에서는 보다 높은 정확도의 비정상행위 판정을 위한 다양한 환경의 로그들을 추출하여 처리에 적합한 형태로 변환하는 전처리 시스템을 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.1336-1339
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• 2009

산업화가 진행된 세계 주요 선진국들은 의학의 발달과 평균 수명의 증가로 고령화의 위기를 겪고 있다. 인구 고령화에 따라 치매 인구도 크게 증가하였다. 치매 인구의 증가는 국가와 가정의 물질적, 인적 비용을 증가시키고 있다. 이와 같은 사회문제를 해결하고 효율적인 치매 환자 관리를 위한 방법이 필요하다. 관찰 대상자가 치매 증상과 비슷하게 행동한다면 치매를 의심해 볼 수 있다. 본 논문에서는 3축 가속도 센서를 사용하여 대상자의 행위 정보를 수집하고 디지털화한다. 디지털화 된 행위정보를 치매 증상의 행동 패턴과 비교하여 관찰 대상자의 행동이 치매 증상인지 정상적인 활동인지 판단할 수 있는 방법을 소개한다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.10 no.4
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• pp.59-71
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• 2000

Due to the advance of computer and communication technology, intrusions or crimes using a computer have been increased rapidly while various information has been provided to users conveniently. As a results, many studies are necessary to detect the activities of intruders effectively. In this paper, a new association algorithm for the anomaly detection model is proposed in the process of generating user\`s normal patterns. It is that more recently observed behavior get more affection on the process of data mining. In addition, by clustering generated normal patterns for each use or a group of similar users, it is possible to identify the usual frequency of programs or command usage for each user or a group of uses. The performance of the proposed anomaly detection system has been tested on various system Parameters in order to identify their practical ranges for maximizing its detection rate.