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摘要翻译：
近年来，随着网络攻击的数量和严重程度的增加，入侵检测系统(IDS)越来越成为网络安全的重要组成部分。由于安全审计数据的海量性以及入侵行为的复杂性和动态性，优化入侵检测系统的性能成为一个重要的开放性问题，越来越受到学术界的重视。探索某些算法是否对某些攻击类别执行得更好的不确定性构成了本文报告的动机。本文利用KDD99数据集对一组综合分类器算法的性能进行了评估。根据评价结果，对每一类攻击选择最佳算法，并提出了两种分类器算法选择模型。仿真结果的比较表明，将所提出的模型应用于不同类型的网络攻击检测，可以取得明显的性能改善和实时入侵检测效果。
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英文标题：
《Application of Data Mining to Network Intrusion Detection: Classifier
Selection Model》
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作者：
Huy Nguyen and Deokjai Choi
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最新提交年份：
2010
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Networking and Internet Architecture 网络和因特网体系结构
分类描述：Covers all aspects of computer communication networks, including network architecture and design, network protocols, and internetwork standards (like TCP/IP). Also includes topics, such as web caching, that are directly relevant to Internet architecture and performance. Roughly includes all of ACM Subject Class C.2 except C.2.4, which is more likely to have Distributed, Parallel, and Cluster Computing as the primary subject area.
涵盖计算机通信网络的所有方面，包括网络体系结构和设计、网络协议和网络间标准（如TCP/IP）。还包括与Internet体系结构和性能直接相关的主题，如web缓存。大致包括除C.2.4以外的所有ACM主题类C.2，后者更有可能将分布式、并行和集群计算作为主要主题领域。
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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。
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英文摘要：
As network attacks have increased in number and severity over the past few years, intrusion detection system (IDS) is increasingly becoming a critical component to secure the network. Due to large volumes of security audit data as well as complex and dynamic properties of intrusion behaviors, optimizing performance of IDS becomes an important open problem that is receiving more and more attention from the research community. The uncertainty to explore if certain algorithms perform better for certain attack classes constitutes the motivation for the reported herein. In this paper, we evaluate performance of a comprehensive set of classifier algorithms using KDD99 dataset. Based on evaluation results, best algorithms for each attack category is chosen and two classifier algorithm selection models are proposed. The simulation result comparison indicates that noticeable performance improvement and real-time intrusion detection can be achieved as we apply the proposed models to detect different kinds of network attacks.
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PDF链接：
https://arxiv.org/pdf/1007.1268
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