对比多种机器学习模型在物联网DDoS检测中的表现。
A Comparative Analysis of Machine Learning Models for DDoS Detection in IoT Networks
- 用XGBoost、KNN等模型从正常流量中识别DDoS攻击
- XGBoost在准确率和召回率上表现最佳，优于其他模型
- 适合需要实时响应的物联网安全系统参考
本文研究了在物联网网络中利用机器学习模型检测DDoS攻击的方法。随着物联网设备的快速扩张，其面临诸多网络安全威胁，而传统安全机制往往不统一且响应滞后。本研究评估了XGBoost、K-近邻（K-Nearest Neighbours）、随机梯度下降（Stochastic Gradient Descent）和朴素贝叶斯（Naïve Bayes）等模型在检测DDoS攻击中的性能，通过准确率、精确率、召回率和F1分数等多项指标进行比较。结果表明，各模型在动态变化的物联网环境中各有优劣，其中XGBoost在多数指标上表现最优，展现出较高的检测效率与稳定性。该分析揭示了机器学习在提升物联网安全体系中的潜力，可实现自适应、高效且可靠的实时威胁检测，为应对现代网络攻击提供有力支持。
原文摘要 · Abstract (English)
This paper presents the detection of DDoS attacks in IoT networks using machine learning models. Their rapid growth has made them highly susceptible to various forms of cyberattacks, many of whose security procedures are implemented in an irregular manner. It evaluates the efficacy of different machine learning models, such as XGBoost, K-Nearest Neighbours, Stochastic Gradient Descent, and Naïve Bayes, in detecting DDoS attacks from normal network traffic. Each model has been explained on several performance metrics, such as accuracy, precision, recall, and F1-score to understand the suitability of each model in real-time detection and response against DDoS threats. This comparative analysis will, therefore, enumerate the unique strengths and weaknesses of each model with respect to the IoT environments that are dynamic and hence moving in nature. The effectiveness of these models is analyzed, showing how machine learning can greatly enhance IoT security frameworks, offering adaptive, efficient, and reliable DDoS detection capabilities. These findings have shown the potential of machine learning in addressing the pressing need for robust IoT security solutions that can mitigate modern cyber threats and assure network integrity.
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