• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.236-246
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• 2008

We consider the Inventory Routing problem(IRP) for the vending machine operating system. An artificial immune system(AIS) is introduced to solve the IRP. The IPR is an rolling wave planning. The previous solution of IRP is one of good initial solution of current IRP. We introduce an Artificial Immune system with memory cell (AISM) which store previous solution in memory cell and use an initial solution for current problem. Experiment results shows that AISM reduced calculations time in relatively less demand uncertainty.

• IE interfaces
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• v.20 no.4
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• pp.458-468
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• 2007

Artificial immune systems (AIS) are one of natural computing inspired by the natural immune system. The fault detection, the pattern recognition, the system control and the optimization are major application area of artificial immune systems. This paper gives a concept of artificial immune systems and useful techniques as like the clonal selection, the immune network theory and the negative selection. A concise survey on the optimization problem based on artificial immune systems is generated. The overall performance of artificial immune systems for the optimization problem is discussed.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.254-257
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• 2002

인터넷 보급의 확산과 전자상거래의 활성화와 유.무선 인터넷의 보급에 따른 악의적인 사이버 공격의 시도의 성공사례가 증가하고 있다. 미로 인해 점차 더 많은 문제가 야기될 것으로 예상된다. 현재 일반적인 인터넷상의 시스템은 악의적인 공격에 적절하게 대응해오지 못하고 있으며, 다른 범용의 시스템들도 기존의 백신 프로그램에 의존하며 그 공격에 대응해오고 있다. 따라서 새로운 침입에 대하여는 대처하기 힘든 단점을 가지고 있다. 본 논문에서는 생체 자율분산시스템의 일부분인 T세포의 positive selection과 negative selection을 이용한 자기/비자기 인식 알고리즘을 제안한다. 제안한 알고리즘은 네트워크 환경에서 침입탐지 시스템에 적용하여 기존에 알려진 침입뿐만 아니라 새로운 침입에 대해서도 대처할 수 있다

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.1022-1024
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• 2022

최근 인공지능 기술이 급격하게 발전하고 빠르게 보급되면서, 머신러닝 시스템을 대상으로 한 다양한 공격들이 등장하기 시작하였다. 인공지능은 많은 강점이 있지만 인위적인 조작에 취약할 수 있기 때문에, 그만큼 이전에는 존재하지 않았던 새로운 위험을 내포하고 있다고 볼 수 있다. 본 논문에서는 데이터 유형 별 적대적 공격 샘플을 직접 제작하고 이에 대한 효과적인 방어법을 구현하였다. 영상 및 텍스트 데이터를 기반으로 한 적대적 샘플공격을 방어하기 위해 적대적 훈련기법을 적용하였고, 그 결과 공격에 대한 면역능력이 형성된 것을 확인하였다.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.4
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• pp.905-915
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• 2012

This paper suggests the online method to identify normal and abnormal state of water quality on the ocean USN. To define normal of the ocean water quality, we utilize the negative selection algorithm of artificial immunity system which has self and nonself identification characteristics. To distinguish abnormal status, normal state set of the ocean water quality needs to be defined. For this purpose, we generate normal state set base on mutations of each data and mutation of the data as logical product. This mutated normal (or self) sets used to identify abnormal status of the water quality. We represent the experimental result about mutated self set with the Gaussian function. Through setting the method on the ocean sensor logger, we can monitor whether the ocean water quality is normal or abnormal state by online.

• Convergence Security Journal
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• v.2 no.2
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• pp.39-47
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• 2002

To recently with the love-letter and Back Orifice the same Worm-virus, with the Trojan and the Linux-virus back against the new species virus which inside and outside of the country to increase tendency the malignant new species virus which is the possibility of decreasing the damage which is enormous in the object appears and to follow a same network coat large scale PC is being quicker, it disposes spontaneously to respect, applied an artificial intelligence technique the research against the next generation malignant computer virus of new form is demanded. Will reach and to respect it analyzes the digital immunity system of the automatic detection which is quick against the next generation malignant virus which had become unconfirmed and the foreign countries which has an removal function.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.391-394
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• 2004

Change of paradigm of network attack technique was begun by fast extension of the latest Internet and new attack form is appearing. But, Most intrusion detection systems detect informed attack type because is doing based on misuse detection, and active correspondence is difficult in new attack. Therefore, to heighten detection rate for new attack pattern, visibilitys to apply human immunity mechanism are appearing. In this paper, we create self-file from normal behavior profile about network packet and embody self recognition algorithm to use self-nonself discrimination in the human immune system to detect anomaly behavior. Sense change because monitors self-file creating anomaly detector based on Negative Selection Algorithm that is self recognition algorithm's one and detects anomaly behavior. And we achieve simulation to use DARPA Network Dataset and verify effectiveness of algorithm through the anomaly detection rate.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.124-127
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• 1997

In this paper, we propose a method of cooperative control based on immune system in distributed autonomous robotic system(DARS). Immune system is living body's self-protection and self-maintenance system. Thus these features can be applied to decision making of optimal swarm behavior in dynamically changing environment. For the purpose of applying immune system to DARS, a robot is regarded as a B lymphocyte(B cell), each environmental condition as an antigen, and a behavior strategy as an antibody respectively. The executing process of proposed method is as follows. When the environmental condition changes, a robot selects an appropriate behavior strategy. And its behavior strategy is simulated and suppressed by other robot using communication. Finally much simulated strategy is adopted as a swarm behavior strategy. This control scheme is based on clonal selection and idiotopic network hypothesis. And it is used for decision making of optimal swarm strategy.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.9
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• pp.402-412
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• 2002

The navigation algorithms enable autonomous mobile robots to reach given target points without collision against obstacles. To achieve safe navigations in unknown environments, this paper presents an effective navigation algorithm for the autonomous mobile robots with ultrasonic sensors. The proposed navigation algorithm consists of an obstacle-avoidance behavior, a target-reaching behavior and a fuzzy-based decision maker. In the obstacle-avoidance behavior and the target-reaching behavior, artificial immune networks are used to select a proper steering angle, make the autonomous mobile robot avoid obstacles and approach a given target point. The fuzzy-based decision maker combines the steering angles from the target-reaching behavior and the obstacle-avoidance behavior in order to steer the autonomous mobile robot appropriately. Simulational and experimental results show that the proposed navigation algorithm is very effective in unknown environments.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.532-532
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• 2000

Conventional artificial intelligence systems are not properly responding under dynamically changing environments. To overcome this problem, reactive planning systems implementing new Al principles, called behavior-based Al or emergent computation, have been proposed and confirmed their usefulness. As another alternative, biological information processing systems may provide many feasible ideas to these problems. Immune system, among these systems, plays important roles to maintain its own system against dynamically changing environments. Therefore, immune system would provide a new paradigm suitable for dynamic problem dealing with unknown environments. In this paper, a new approach to behavior-based Al by paying attention to biological immune system is investigated. The feasibility of this method is confirmed by applying to behavior control of an autonomous mobile robot in cluttered environment.