| 用于异常检测的单级免疫学习算法 |
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| 引用本文： | 王宏宇,满成城. 用于异常检测的单级免疫学习算法[J]. 华东理工大学学报(自然科学版), 2006, 32(8): 980-984 |
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| 作者姓名： | 王宏宇 满成城 |
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| 作者单位： | 石家庄职业技术学院计算机工程系,石家庄,050081;华东理工大学信息科学与工程学院,上海,200237 |
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| 摘 要： | 基于对多级免疫学习算法(M ILA)的批评性研究,提出了单级免疫学习算法(SILA)。该算法适用于低维度特征量的异常检测,提高了探测器训练的效率和效益,而且对M ackey-G lass时间序列数据的检测取得了很好的实验结果。
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| 关 键 词： | 人工免疫系统 阴性选择 MILA 异常检测 实数表示 |
| 文章编号： | 1006-3080（2006）08-0980-05 |
| 收稿时间： | 2005-12-06 |
| 修稿时间： | 2005-12-06 |
A Single-Level Immune Learning Algorithm for Anomaly Detection |
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| WANG Hon-gyu,MAN Cheng-cheng. A Single-Level Immune Learning Algorithm for Anomaly Detection[J]. Journal of East China University of Science and Technology, 2006, 32(8): 980-984 |
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| Authors: | WANG Hon-gyu MAN Cheng-cheng |
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| Abstract: | Anomaly detection is one of the main issues in ensuring computer security.Various artificial immune system(AIS) algorithms,from negative selection algorithm (NSA) to multilevel immune learning algorithm(MILA),are therefore developed to serve this purpose.Based on a critical study of the MILA approach,this paper proposes a single-level immune learning algorithm(SILA),which extends the ideas of MILA and pays more attentions to the problem space.The proposed algorithm contributes mainly to(improving) the effectiveness and efficiency of detector training,which is of great concern in all artificial(immune) systems. |
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| Keywords: | artificial immune system negative selection MILA anomaly detection real-valued representation |
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