摘要翻译：
提出了一种新的基于朴素贝叶斯分类器和决策树的自适应网络入侵检测学习算法，该算法对不同类型的网络攻击进行平衡检测，使误报保持在可接受的水平，并从训练数据中消除导致检测模型复杂的冗余属性和矛盾实例。该算法还解决了数据挖掘中的一些难点，如连续属性处理、缺失属性值处理、训练数据降噪等。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，在过去的几十年里，基于数据挖掘的入侵检测技术已经被应用于基于网络的流量数据和基于主机的数据。然而，目前的入侵检测系统(IDS)还存在着许多需要研究的问题。在KDD99基准入侵检测数据集上，我们用已有的学习算法对本文提出的算法进行了性能测试。实验结果表明，该算法在有限的计算资源下，对不同类型的网络入侵均取得了较高的检测率(DR)和显著降低误报(FP)。
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英文标题：
《Combining Naive Bayes and Decision Tree for Adaptive Intrusion Detection》
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作者：
Dewan Md. Farid(1), Nouria Harbi(1), and Mohammad Zahidur Rahman(2),
  ((1)University Lumiere Lyon 2 - France, (2)Jahangirnagar University,
  Bangladesh)
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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：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：
  In this paper, a new learning algorithm for adaptive network intrusion detection using naive Bayesian classifier and decision tree is presented, which performs balance detections and keeps false positives at acceptable level for different types of network attacks, and eliminates redundant attributes as well as contradictory examples from training data that make the detection model complex. The proposed algorithm also addresses some difficulties of data mining such as handling continuous attribute, dealing with missing attribute values, and reducing noise in training data. Due to the large volumes of security audit data as well as the complex and dynamic properties of intrusion behaviours, several data miningbased intrusion detection techniques have been applied to network-based traffic data and host-based data in the last decades. However, there remain various issues needed to be examined towards current intrusion detection systems (IDS). We tested the performance of our proposed algorithm with existing learning algorithms by employing on the KDD99 benchmark intrusion detection dataset. The experimental results prove that the proposed algorithm achieved high detection rates (DR) and significant reduce false positives (FP) for different types of network intrusions using limited computational resources.
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PDF链接：
https://arxiv.org/pdf/1005.4496