摘要翻译：
人工免疫系统，更具体地说是否定选择算法，以前已经应用于入侵检测。本研究的目的是基于免疫学中的一个新概念--危险理论来开发一个入侵检测系统。树突状细胞（Dendritic Cells，DCs)是抗原提呈细胞，是激活宿主组织信号的关键，并将这些信号与抗原蛋白相关联。在算法上，单个DCs基于时间窗进行多传感器数据融合。整个DCs种群异步地将融合信号与辅助数据流相关联。人类DC的行为被抽象成DC算法(DCA)，该算法使用免疫启发框架LibTissia来实现。该系统用于检测基本机器学习数据集的上下文切换和实时检测传出的portscans。实验结果表明，传出的portscan与正常通信量之间存在显著差异。
---
英文标题：
《Dendritic Cells for Anomaly Detection》
---
作者：
Julie Greensmith, Jamie Twycross, Uwe Aickelin
---
最新提交年份：
2010
---
分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
--
一级分类：Computer Science        计算机科学
二级分类：Neural and Evolutionary Computing        神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
--

---
英文摘要：
  Artificial immune systems, more specifically the negative selection algorithm, have previously been applied to intrusion detection. The aim of this research is to develop an intrusion detection system based on a novel concept in immunology, the Danger Theory. Dendritic Cells (DCs) are antigen presenting cells and key to the activation of the human signals from the host tissue and correlate these signals with proteins know as antigens. In algorithmic terms, individual DCs perform multi-sensor data fusion based on time-windows. The whole population of DCs asynchronously correlates the fused signals with a secondary data stream. The behaviour of human DCs is abstracted to form the DC Algorithm (DCA), which is implemented using an immune inspired framework, libtissue. This system is used to detect context switching for a basic machine learning dataset and to detect outgoing portscans in real-time. Experimental results show a significant difference between an outgoing portscan and normal traffic.
---
PDF链接：
https://arxiv.org/pdf/1001.2411