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 Na\"ive 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.

翻译：本文提出利用机器学习模型检测物联网网络中的DDoS攻击。物联网网络的快速增长使其极易受到各类网络攻击，且许多安全措施的实施方式并不规范。本研究评估了XGBoost、K近邻、随机梯度下降和朴素贝叶斯等不同机器学习模型在从正常网络流量中检测DDoS攻击方面的效能。通过准确率、精确率、召回率和F1分数等多个性能指标对各模型进行解析，以理解各模型在实时检测和响应DDoS威胁中的适用性。本比较分析将系统阐述各模型在动态演进的物联网环境中所具备的独特优势与局限。通过对这些模型效能的深入剖析，揭示了机器学习如何显著增强物联网安全框架，提供自适应、高效且可靠的DDoS检测能力。研究结果证明了机器学习在应对物联网安全迫切需求方面的潜力，能够有效缓解现代网络威胁并保障网络完整性。