Hybrid Feature Selection for Efficient Detection of DDoS Attacks in IoT
Hybrid Feature Selection for Efficient Detection of DDoS Attacks in IoT
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DOI：
10.1145/3556677.3556687
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Liang Hong;Khadijeh Wehbi;Tulha Hasan Alsalah
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文献类型：
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作者：
Liang Hong;Khadijeh Wehbi;Tulha Hasan Alsalah
物联网（IoT）上越来越多的分布式拒绝服务（DDoS）攻击导致需要一种有效的检测方法。虽然已经进行了大量研究来检测传统网络上的DDoS攻击，例如基于机器学习（ML）的方法提高了准确性和置信度，但物联网网络中有限的带宽和计算资源限制了ML的应用，特别是基于深度学习（DL）的解决方案，需要大量的输入数据。为了妥善解决资源受限的物联网网络中的安全问题，本文旨在通过从原始特征中提取最相关特征的子集来减少输入数据维度，并使用该子集来检测物联网上的DDoS攻击，而不会降低检测性能。开发了一种具有成本效益的模型，用于在降维之前清洗和准备原始数据。一个混合的特征选择，使用互信息（MI），方差分析（ANOVA），卡方，基于L1的特征选择，和基于树的特征选择算法的设计，以确定重要的数据特征，并减少检测所需的数据输入。仿真结果表明，所提出的混合特征选择方法选择的特征组合，提高了检测精度。训练时间远少于每个单独特征选择方法的组合。
The increasing Distributed Denial of Service (DDoS) attacks on the Internet of Things (IoT) is leading to the need for an efficient detection approach. Although much research has been conducted to detect DDoS attacks on traditional networks, such as machine learning (ML) based approaches that have improved accuracy and confidence, the limited bandwidth and computation resources in IoT networks restrict the application of ML, especially deep learning (DL) based solutions that require extensive input data. In order to appropriately address the security issues in the resources-constrained IoT network, this paper is aimed to reduce the input data dimensions by extracting a subset of the most relevant features from the original features and using this subset to detect DDoS attacks on IoT without degrading the detection performance. A cost-effective model is developed to clean and prepare raw data before dimensionality reduction. A hybrid feature selection that uses Mutual Information (MI), Analysis of Variance (ANOVA), Chi-Squared, L1-based feature selection, and Tree-based feature selection algorithms is designed to identify important data features and reduce the data inputs needed for detection. Simulation results show that detection accuracy is improved with the combination of features chosen by the proposed hybrid feature selection approach. The training time is much less than the combination of each individual feature selection method.