• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2001년도 추계학술발표논문집 (하)
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• pp.1025-1028
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• 2001

인터넷의 확산과 이용자의 급증으로 웜바이러스에 대한 문제가 최근에 크게 대두되고 있다. 스크립트 형 웜바이러스의 경우 제작이 쉬워 누구나 몇 시간의 학습을 통하여 바이러스를 제작찬 수 있다. 이러한 문제의 심각성은 최근 7개월 동안의 바이러스 통계에서도 나타나는데 전체 바이러스 중 평균 22.5%를 차지하고 있다. 이러한 웜바이러스를 차단하기 위해서 여러 가지 방법들이 사용되고 있으나 E-mail로 급속하게 퍼지는 웜바이러스의 확산을 차단하기 위해서 네트워크 기반의 시스템 보호방법이 요구되어지고 있다. 이에 본 논문에서는 알려지지 않은 웜바이러스로부터 내부 네트워크를 방어하기 위한 면역시스템을 제안한다. 자동화된 면역 시스템은 분산된 각각의 웜바이러스 탐지 시스템들이 새로운 바이러스 정보를 동적으로 공유할 수 있도록 하여 새로운 바이러스로부터 해당 시스템과 그 시스템이 속해 있는 내부 네트워크를 바이러스로부터 보호할 수 있도록 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권11호
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• pp.5574-5591
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• 2017

Inspired by the idea of Artificial Immune System, many researches of wireless sensor network (WSN) intrusion detection is based on the artificial intelligent system (AIS). However, a large number of generated detectors, black hole, overlap problem of NSA have impeded further used in WSN. In order to improve the anomaly detection performance for WSN, detector generation mechanism need to be improved. Therefore, in this paper, a Differential Evolution Constraint Multi-objective Optimization Problem based Negative Selection Algorithm (DE-CMOP based NSA) is proposed to optimize the distribution and effectiveness of the detector. By combining the constraint handling and multi-objective optimization technique, the algorithm is able to generate the detector set with maximized coverage of non-self space and minimized overlap among detectors. By employing differential evolution, the algorithm can reduce the black hole effectively. The experiment results show that our proposed scheme provides improved NSA algorithm in-terms, the detectors generated by the DE-CMOP based NSA more uniform with less overlap and minimum black hole, thus effectively improves the intrusion detection performance. At the same time, the new algorithm reduces the number of detectors which reduces the complexity of detection phase. Thus, this makes it suitable for intrusion detection in WSN.

• 대한전기학회논문지:전력기술부문A
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• 제51권9호
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• pp.441-450
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• 2002

In this paper, optimal tuning problem of power system stabilizer using IA-QFT is investigated to improve power system dynamic stability in spite of parameter variation and disturbance uncertainties. The most important feature of QFT is that it is able to deal with the design problem of complicated uncertain plants. However, loop shaping is currently performed in computer aided design environments manually and it is usually a trial and error procedure. It is difficult to design a controller to satisfy all specifications manually. To solve this problem, a study of design automation using IA needs to be taken into account. The robustness of the proposed controller has been investigated on a single machine infinite bus model. The results are shown that the proposed PSS using IA-QFT is more robust than conventional PSS.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 춘계학술대회 학술발표 논문집
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• pp.167-170
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• 2001

인터넷 보안문제 해결을 위해서 침입탐지 시스템 및 바이러스 백신 등이 연구되어 왔고, 생체면역을 응용한 보안체계가 연구되고 있으나, 컴퓨터 자원소비와 비실시간대응의 문제점을 가지고 있다. 이에 Antibody Layer[1]는 인공면역과 Host alliance를 기반으로 하여 보안문제 해결에 효율성과 정확성을 제공하였다. 본 논문에서는 Antibody Layer의 Host alliance를 위한 Cooperation Protocol에 대하여 논하였다.

• 한국멀티미디어학회논문지
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• 제19권8호
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• pp.1361-1370
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• 2016

To reduce huge losses in pig farms, weaning pigs with weak immune systems are required to be carefully supervised. Even if various researches have been performed for pig monitoring environment, segmenting each pig from touching-pigs is still entrenched as a difficult problem. In this paper, we propose a segmentation method for touching-pigs by using concave-points and edge information in a video surveillance system. Especially, we interpret the segmentation problem as a time-series analysis problem in order to identify the concave-points generated by touching-pigs. Based on the experimental results with the videos obtained from a domestic pig farm, we believe that the proposed method can accurately segment the touching-pigs.

• 한국지능시스템학회논문지
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• 제13권1호
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• pp.119-124
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• 2003

본 논문에서는 생체의 면역계에서 중요한 역할을 하는 세포독성 T세포의 생성과정의 하나인 긍정 선택(positive selection)과 부정 선택(negative selection)을 모델링하였다. 그리고 침입에 의한 데이터 변경과 바이러스에 의한 데이터 감염 등을 탐지할 때 가장 중요한 요소인 변경 검사 알고리즘을 개발하였다. 제안된 방법은 자기를 인식하는 MHC 인식부와 비자기를 인식하는 항원 인식부를 생성하는 알고리즘이다. 따라서 제안한 방법은 실제 면역세포와 마찬가지로 자신과 침입자 모두에 대한 인식기를 가지고 변경을 탐지하게 된다. 시뮬레이션을 통하여 자기파일의 국소가 변경되었을 때와 블록이 변경되었을 때에 대하여 두 가지 방법을 이용한 변경 검사 알고리즘의 특성과 유효성을 밝힌다

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제4권1호
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• pp.7-11
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• 2004

This paper proposes a new immunotronic approach for the fault detection in hardware. The suggested method is, inspired by biology and its implementation is based on genetic algorithm. Tolerance conditions in the immunotronic system for fault detection correspond to the antibodies in the biological immune system. A novel algorithm of generating tolerance conditions is suggested based on the principle of the antibody diversity and GA optimization is employed to select mature tolerance conditions in immunotronic fault detection system. The suggested method is applied to the fault detection for MCNC benchmark FSMs (finite state machines) and its effectiveness is demonstrated by the computer simulation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권5호
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• pp.2732-2753
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• 2019

With the application of wireless sensor networks in the fields of ecological observation, defense military, architecture and urban management etc., the security problem is becoming more and more serious. Characteristics and constraint conditions of wireless sensor networks such as computing power, storage space and battery have brought huge challenges to protection research. Inspired by the danger theory in biological immune system, this paper proposes an intrusion detection model for wireless sensor networks. The model abstracts expressions of antigens and antibodies in wireless sensor networks, defines meanings and functions of danger signals and danger areas, and expounds the process of intrusion detection based on the danger theory. The model realizes the distributed deployment, and there is no need to arrange an instance at each sensor node. In addition, sensor nodes trigger danger signals according to their own environmental information, and do not need to communicate with other nodes, which saves resources. When danger is perceived, the model acquires the global knowledge through node cooperation, and can perform more accurate real-time intrusion detection. In this paper, the performance of the model is analyzed including complexity and efficiency, and experimental results show that the model has good detection performance and reduces energy consumption.

• Journal of applied mathematics & informatics
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• 제41권6호
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• pp.1257-1274
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• 2023

Rotating machines heavily rely on an intricate network of interconnected sub-components, with bearing failures accounting for a substantial proportion (40% to 90%) of all such failures. To address this issue, intelligent algorithms have been developed to evaluate vibrational signals and accurately detect faults, thereby reducing the reliance on expert knowledge and lowering maintenance costs. Within the field of machine learning, Artificial Immune Systems (AIS) have exhibited notable potential, with applications ranging from malware detection in computer systems to fault detection in bearings, which is the primary focus of this study. In pursuit of this objective, we propose a novel procedure for detecting novel instances of anomalies in varying operating conditions, utilizing only the signals derived from the healthy state of the analyzed machine. Our approach incorporates AIS augmented by Dynamic Time Warping (DTW). The experimental outcomes demonstrate that the AIS-DTW method yields a considerable improvement in anomaly detection rates (up to 53.83%) compared to the conventional AIS. In summary, our findings indicate that our method represents a significant advancement in enhancing the resilience of AIS-based novelty detection, thereby bolstering the reliability of rotating machines and reducing the need for expertise in bearing fault detection.

• 융합보안논문지
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• 제2권2호
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• pp.39-47
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• 2002

최근에는 러브레터 ,백오리피스와 같은 웜바이러스, 트로이목마, 리눅스바이러스 등과 같은 네트워크 상의 대규모 PC를 대상으로 막대한 피해를 줄 수 있는 악성 신종 바이러스가 출현함에 따라 국내외적으로 증가추세에 있는 신종 바이러스에 대하여 보다 신속하고 능동적으로 대처하기 위해서는 인공지능기법을 적용한 새로운 형태의 차세대 악성 컴퓨터바이러스에 대한 연구가 요구된다. 이를 위해서 미확인된 차세대 악성 바이러스에 대한 신속한 자동탐지 및 제거기능을 갖는 해외의 디지털 면역시스템을 분석한다.