物联网网络中使用监督学习的本地入侵检测实验

2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS) Pub Date : 2020-05-01 DOI:10.1109/DCOSS49796.2020.00073

Christiana Ioannou, V. Vassiliou

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引用次数: 7

摘要

在本文中，我们正在试验用于物联网的入侵检测系统(IDS)。正在考虑的IDS采用机器学习技术来检测物联网网络中的新型攻击。我们检查基于支持向量机(SVM)的检测。使用物联网测试平台数据对选择性转发和黑洞网络路由层攻击的检测模型进行了训练和评估，准确率高达99.8%，召回率为100%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Experimentation with Local Intrusion Detection in IoT Networks Using Supervised Learning

In this paper we are experimenting with an intrusion detection system (IDS) for IoT. The IDS under consideration is employing a machine learning techniques for detecting novel at-tacks in the IoT network. We examine detection based on Support Vector Machines (SVM). The detection models were trained and evaluated for Selective Forward and Blackhole network routing layer attacks using IoT-testbed data and achieved up to 99.8% Accuracy rates and 100% Recall values.

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2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS)

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