基于Agent的分类模型
An Agent Based Classification Model
摘要：
该模型的主要功能是访问UCI Wisconsin Breast Cancer数据集[1]，并将数据项分为正常和异常两类。这种分类可以被称为异常检测，其区分计算机系统中的异常行为与正常行为。异常检测的一种流行解决方案是免疫系统（AIS）。人工免疫系统是受理论免疫学和观察到的免疫功能、原理和模型的启发，应用于问题解决的自适应系统。树突细胞算法（DCA）[2]是一种AIS算法，专门为异常检测而开发。它已成功地应用于计算机安全领域的入侵检测。基于智能体的模型被认为是实现AIS的理想方法，因为智能体可以完美地代表AIS中的免疫实体。该模型评估了在基于代理的仿真环境中重新实现DCA的可行性，该环境称为AnyLogic，其中DCA中的免疫实体由智能代理表示。如果该模型能够成功实现，将为在基于Agent的仿真环境中实现更复杂、更适应性更强的AIS模型提供可能。
英文摘要：
The major function of this model is to access the UCI Wisconsin Breast Can- cer data-set[1] and classify the data items into two categories, which are normal and anomalous. This kind of classifi cation can be referred as anomaly detection, which discriminates anomalous behaviour from normal behaviour in computer systems. One popular solution for anomaly detection is Artifi cial Immune Sys- tems (AIS). AIS are adaptive systems inspired by theoretical immunology and observed immune functions, principles and models which are applied to prob- lem solving. The Dendritic Cell Algorithm (DCA)[2] is an AIS algorithm that is developed specifi cally for anomaly detection. It has been successfully applied to intrusion detection in computer security. It is believed that agent-based mod- elling is an ideal approach for implementing AIS, as intelligent agents could be the perfect representations of immune entities in AIS. This model evaluates the feasibility of re-implementing the DCA in an agent-based simulation environ- ment called AnyLogic, where the immune entities in the DCA are represented by intelligent agents. If this model can be successfully implemented, it makes it possible to implement more complicated and adaptive AIS models in the agent-based simulation environment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
