Distributed denial-of-service attacks are still difficult to handle as per current scenarios. The attack aim is a menace to network security and exhausting the target networks with malicious traffic from multiple sites. Although a plethora of conventional methods have been proposed to detect DDoS attacks, so far the rapid diagnosis of these attacks using feature selection algorithms is a daunting challenge. The proposed system uses a hybrid methodology for selecting features by applying feature selection methods on machine learning classifiers. Feature selections methods, namely chi-square, Extra Tree and ANOVA have been applied on four classifiers Random Forest, Decision Tree, k-Nearest Neighbors and XGBoost for early detection of DDoS attacks on IoT devices. We use the CICDDoS2019 dataset containing comprehensive DDoS attacks to train and assess the proposed methodology in a cloud-based environment (Google Colab). Based on the experimental results, the proposed hybrid methodology provides superior performance with a feature reduction ratio of 82.5% by achieving 98.34% accuracy with ANOVA for XGBoost and helps in early detection of DDoS attacks on IoT devices.

按照目前的情况，分布式拒绝服务攻击仍然难以处理。攻击的目的是威胁网络安全，并通过来自多个站点的恶意流量耗尽目标网络。尽管已经提出了大量传统方法来检测 DDoS 攻击，但到目前为止，使用特征选择算法对这些攻击进行快速诊断是一项艰巨的挑战。所提出的系统通过在机器学习分类器上应用特征选择方法来使用混合方法来选择特征。特征选择方法，即卡方、额外树和方差分析已应用于四个分类器随机森林、决策树、k- 最近邻居和 XGBoost 用于及早检测物联网设备上的 DDoS 攻击。我们使用包含全面 DDoS 攻击的 CICDDoS2019 数据集在基于云的环境 (Google Colab) 中训练和评估所提出的方法。根据实验结果，所提出的混合方法通过使用 ANOVA for XGBoost 实现 98.34% 的准确度，提供了卓越的性能，特征减少率为 82.5%，并有助于早期检测物联网设备上的 DDoS 攻击。