Earticle

In this paper, a novel negative selection algorithm for recognition problems was given. Compared with the traditional negative selection algorithm, a co-stimulation signal was added to start the detectors, which a key factor in immune response. Co-stimulation signal was calculated by the techniques of the statistics and the sliding window, which not only reduced time complexity of algorithm but also improved accuracy of the algorithm. Entropy was adopted to evaluate the density of detectors for optimizing the coverage of nonself area. Experiment results proved high accuracy and efficiency of the proposed algorithm.

Segmentation is a efficient technique of dividing the image into different regions or segments. Most of the researchers took clustering as the best method of segmenting an image. In clustering we try to increase the similarity within a same class and decrease the similarity between the classes. Many clustering algorithms were developed like FCM, FLICM and FELICM which are considered as the best algorithms to cluster the data. In our paper, we combine FELICM (Fuzzy Edge and Local Information C-Mean) with the negative selection algorithm. Negative selection algorithm is an evolutionary method which is based on artificial immune systems. The proposed method result shows us high accuracy results and even solves the problem of over segmentation.

Traditional negative selection algorithms often result in a number of black holes, which directly leads to the missing alarm drawback in the intrusion detection system. In order to settle the above problem, a novel negative selection algorithm based on Minkowsky distance is proposed. Firstly, the proposed algorithm computes the Minkowsky distance between the detectors. Then, compute the serial same numbers between the detector and self-set strings, which is helpful to improve the coverage area of the detector. Finally, the new detectors after training and renewal are put into the mature detector set to decline the number of black holes. Experimental results demonstrate that, compared with the traditional negative selection algorithms, the number of black holes and the missing alarm rate decline a lot in the proposed algorithm.

According to revealing the DNA sequence of human and living things, it increases that a demand on a new computational processing method which utilizes DNA sequence information. In this paper we propose a classification algorithm based on negative selection of the immune system to classify DNA patterns. Negative selection is the process to determine an antigenic receptor that recognize antigens, nonself cells. The immune cells use this antigen receptor to judge whether a self or not. If one composes n group of antigenic receptor for n different patterns, they can classify into n patterns. In this paper we propose a pattern classification algorithm based on negative selection in nucleotide base level and amino acid level.

We propose a pattern classification algorithm using self-nonself discrimination principle of immune cells and apply it to DNA pattern classification problem. Pattern classification problem in bioinformatics is very important and frequent one. In this paper, we propose a classification algorithm based on the negative selection of the immune system to classify DNA patterns. The negative selection is the process to determine an antigenic receptor that recognize antigens, nonself cells. The immune cells use this antigen receptor to judge whether a self or not. If one composes ${\eta}$ groups of antigenic receptor for ${\eta}$ different patterns, these receptor groups can classify into ${\eta}$ patterns. We propose a pattern classification algorithm based on the negative selection in nucleotide base level and amino acid level. Also to show the validity of our algorithm, experimental results of RNA group classification are presented.

인터넷의 급속한 확장으로 인해 네트워크 공격기법의 패러다임의 변화가 시작되었으며 새로울 공격 형태가 나타나고 있으나 대부분의 침입 탐지 기술은 오용 탐지 기술을 기반으로 하는 시스템이주를 이루고 있어 알려진 공격 유형만을 탐지하고, 새로운 공격에 능동적인 대응이 어려운 실정이다. 이에 새로운 공격 유형에 대한 탐지력을 높이기 위해 인체 면역 메커니즘을 적용하려는 시도들이 나타나고 있다. 본 논문에서는 데이터 마이닝 기법을 이용하여 네트워크 패킷에 대한 정상 행위 프로파일을 생성하고 생성된 프로파일을 자기공간화 하여 인체면역계의 자기, 비자기 구분기능을 이용해 자기 인식 알고리즘을 구현하여 이상행위를 탐지하고자 한다. 자기인식 알고리즘의 하나인 Negative Selection Algorithm을 기반으로 anomaly detector를 생성하여 자기공간을 모니터하여 변화를 감지하고 이상행위를 검출한다. DARPA Network Dataset을 이용하여 시뮬레이션을 수행하여 침입 탐지율을 통해 알고리즘의 유효성을 검증한다.

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.