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Intrusion detection technique using Coarse Gaussian SVM

机译:使用粗加斯SVM的入侵检测技术

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摘要

In the new era of internet technology, everybody is transferring data from place to place through the internet. As internet technology is improving, different types of attacks have also increased. To detect the attacks it is important to protect transmitted information. The role of Intrusion Detection System (IDS) is very imperative to detect various types of attacks. Although researchers have proposed numerous theories and methods in the area of IDS, the research in area of intrusion detection is still going on. In this paper, Coarse Gaussian Support Vector Machine (CGSVM) based intrusion detection technique is proposed. The proposed method has four major steps namely, Data Collection, Pre-processing and Studying data, Training and Testing using CGSVM, and Decisions. In implementation, KDDcup99 data sets are used as a benchmark and MATLAB programming environment is used. The results of the simulation are presented by Receiver Operating Characteristics (ROC) and Confusion Matrix. The proposed method achieved detection rates as high 99.99%, 99.95%, 99.53%, 99.19%, 90.57% for DOS, Normal, Probe, R 2 L, U 2 R respectively.
机译:在互联网技术的新时代,每个人都正在通过互联网从地点转移数据。随着互联网技术的改善,不同类型的攻击也增加了。要检测到攻击,保护传输信息非常重要。入侵检测系统(IDS)的作用非常迫切地检测各种类型的攻击。尽管研究人员提出了ID领域的许多理论和方法,但入侵检测领域的研究仍在继续。本文提出了基于粗加斯支持向量机(CGSVM)的入侵检测技术。该方法具有四个主要步骤,即使用CGSVM的数据收集,预处理和研究数据,培训和测试以及决策。在实现中,kddcup99数据集用作基准,使用MATLAB编程环境。通过接收器操作特性(ROC)和混淆矩阵呈现模拟结果。该方法的检测率为99.99%,99.95%,99.95%,99.53%,99.19%,90.57%,分别为DOS,正常,探针,R 2 L,U 2 R。

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