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首页> 外文学位 >WiFi Miner: An online apriori and sensor based wireless network Intrusion Detection System.
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WiFi Miner: An online apriori and sensor based wireless network Intrusion Detection System.

机译：WiFi Miner：基于在线先验和传感器的无线网络入侵检测系统。

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摘要

This thesis proposes an Intrusion Detection System, WiFi Miner, which applies an infrequent pattern association rule mining Apriori technique to wireless network packets captured through hardware sensors for purposes of real time detection of intrusive or anomalous packets. Contributions of the proposed system includes effectively adapting an efficient data mining association rule technique to important problem of intrusion detection in a wireless network environment using hardware sensors, providing a solution that eliminates the need for hard-to-obtain training data in this environment, providing increased intrusion detection rate and reduction of false alarms.;The proposed system, WiFi Miner, solution approach is to find frequent and infrequent patterns on pre-processed wireless connection records using infrequent pattern finding Apriori algorithm also proposed by this thesis. The proposed Online Apriori-Infrequent algorithm improves the join and prune step of the traditional Apriori algorithm with a rule that avoids joining itemsets not likely to produce frequent itemsets as their results, thereby improving efficiency and run times significantly. A positive anomaly score is assigned to each packet (record) for each infrequent pattern found while a negative anomaly score is assigned for each frequent pattern found. So, a record with final positive anomaly score is considered as anomaly based on the presence of more infrequent patterns than frequent patterns found.;Keywords. Data mining, wireless network intrusion detection, Apriori, infrequent patterns, training data.
机译：本文提出了一种入侵检测系统WiFi Miner，该系统将不频繁的模式关联规则挖掘Apriori技术应用于通过硬件传感器捕获的无线网络数据包，以实时检测入侵或异常数据包。拟议系统的贡献包括有效地将有效的数据挖掘关联规则技术适应使用硬件传感器的无线网络环境中的入侵检测的重要问题，提供一种解决方案，从而消除了在该环境中无需获取训练数据的需要，本文提出的WiFi Miner系统解决方案是利用不频繁模式查找Apriori算法在预处理的无线连接记录中查找频繁模式和不频繁模式。提出的Online Apriori-Infrequent算法通过避免加入不太可能产生频繁项目集作为结果的项目集的规则，改进了传统Apriori算法的加入和修剪步骤，从而显着提高了效率和运行时间。为每个发现的不频繁模式分配一个正异常分数给每个数据包（记录），为每个发现的频繁模式分配一个负异常分数。因此，具有最终正异常分数的记录被认为是基于比发现的频繁模式更多的不频繁模式而出现的异常。数据挖掘，无线网络入侵检测，Apriori，不常见模式，训练数据。

著录项

  • 作者

    Rahman, S S Ahmedur.;

  • 作者单位

    University of Windsor (Canada).;

  • 授予单位 University of Windsor (Canada).;
  • 学科 Computer Science.
  • 学位 M.Sc.
  • 年度 2008
  • 页码 125 p.
  • 总页数 125
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 自动化技术、计算机技术;
  • 关键词

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