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A new intrusion detection system based on the combination of support vectors and ant colony: Algorithm and implementation.

机译：一种基于支持向量和蚁群相结合的新型入侵检测系统：算法与实现。

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摘要

To meet the challenges of detecting increasing types of attacks in high-speed network, the thesis contributes on the area of intrusion detection using machine learning methods. By modifying and combining two existing algorithms, i.e. SVM (Support Vector Machine, a supervised learning algorithm for binary classification) and CSOACN (Clustering around Self-Organized Ant Colony Network, an unsupervised learning algorithm for clustering), a new algorithm and a new intrusion detection system (IDS) are proposed and developed.;Key words: Network security, network attack, Intrusion Detection Systems (IDS), data mining, machine learning, real time detection, Object-Oriented Programming.;The performance of the new IDS is evaluated with a commonly applied benchmark data set, i.e. the 1998 DARPA data set. Our experiment results indicate that the combination algorithm is better than the pure SVM in terms of higher average detection rate as well as lower rates of both negative and positive false and better than the pure CSOACN in term of less training time with comparable detection rate as well as comparable rates of negative and positive false. In addition, the effectiveness of the new algorithm is comparable to the KDD99 winner. As a future work on this study, the new IDS will be further improved and be further evaluated by transplanting it onto other types of systems.
机译：为了应对在高速网络中检测不断增加的攻击类型的挑战，本文致力于使用机器学习方法进行入侵检测。通过修改和组合两种现有算法，即SVM（支持向量机，一种用于二进制分类的监督学习算法）和CSOACN（围绕自组织蚁群网络的聚类，一种用于聚类的无监督学习算法），一种新算法和一种新入侵技术关键词：网络安全，网络攻击，入侵检测系统（IDS），数据挖掘，机器学习，实时检测，面向对象程序设计。关键词：使用通用基准数据集（即1998 DARPA数据集）进行评估。我们的实验结果表明，该组合算法在较高的平均检测率和较低的正负误报率方面优于纯SVM，并且在较少的训练时间和可比较的检测率方面优于纯CSOACN。作为可比的阴性和阳性假率。此外，新算法的有效性可与KDD99获奖者媲美。作为这项研究的未来工作，将通过将新的IDS移植到其他类型的系统上来进一步改进和评估它们。

著录项

  • 作者

    Zhang, Qinglei.;

  • 作者单位

    Trent University (Canada).;

  • 授予单位 Trent University (Canada).;
  • 学科 Mathematics.;Computer Science.
  • 学位 M.Sc.
  • 年度 2009
  • 页码 131 p.
  • 总页数 131
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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