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Analysis of Support Vector Machine-based Intrusion Detection Techniques

机译：基于支持向量机的入侵检测技术分析

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摘要

From the last few decades, people do various transaction activities like air ticket reservation, online banking, distance learning,group discussion and so on using the internet. Due to explosive growth of information exchange and electronic commerce inthe recent decade, there is a need to implement some security mechanisms in order to protect sensitive information. Detectionof any intrusive behavior is one of the most important activity for protecting our data and assets. Various intrusion detectionsystems are incorporated in the network for detecting intrusive behavior. In this paper, an analytical study of support vectormachine (SVM)-based intrusion detection techniques is presented. Here, the methodology involves four major steps, namely,data collection, preprocessing, SVM technique for training and testing and decision. The simulated results have been analyzedbased on overall detection accuracy, Receiver Operating Characteristic and (ROC) Confusion Matrix. NSL-KDD dataset isused to analyze the performance of SVM techniques. NSL-KDD dataset is a benchmark for intrusion detection technique andcontains huge amount of network records. The analyzed results show that Linear SVM, Quadratic SVM, Fine Gaussian SVMand Medium Gaussian SVM give 96.1%, 98.6%, 98.7% and 98.5% overall detection accuracy, respectively.
机译：在过去的几十年中，人们使用互联网进行各种交易活动，例如机票预订，网上银行，远程学习，小组讨论等。由于近十年来信息交换和电子商务的爆炸性增长，需要实施一些安全机制来保护敏感信息。检测任何侵入性行为是保护我们的数据和资产的最重要活动之一。网络中集成了各种入侵检测系统，用于检测入侵行为。本文对基于支持向量机（SVM）的入侵检测技术进行了分析研究。在这里，该方法涉及四个主要步骤，即数据收集，预处理，用于训练，测试和决策的SVM技术。基于整体检测精度，接收器工作特性和（ROC）混淆矩阵对仿真结果进行了分析。 NSL-KDD数据集用于分析SVM技术的性能。 NSL-KDD数据集是入侵检测技术的基准，并且包含大量的网络记录。分析结果表明，线性SVM，二次SVM，精细高斯SVM和中高斯SVM分别使整体检测精度达到96.1％，98.6％，98.7％和98.5％。

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