用机器学习实时识别网络攻击，准确率高且响应快。
An Efficient Real Time DDoS Detection Model Using Machine Learning Algorithms
- 基于主成分分析降维，用多种算法对比检测
- 随机森林等模型准确率超95%，检测耗时低于0.1秒
- 适合需要快速防御的云服务与关键系统
分布式拒绝服务攻击已成为企业和政府的重大威胁，导致严重财务损失。随着对互联网服务依赖加深，此类攻击通过伪造流量淹没服务器，引发服务中断和数据泄露。尽管已有多种检测技术，但时间效率与准确率之间的权衡仍使有效方法选择困难。本研究利用UNB CICDDoS2019数据集，基于多种流量特征，采用逻辑回归、K近邻、随机森林、支持向量机、朴素贝叶斯等机器学习分类器，构建高效实时的DDoS检测系统。数据经清洗、标准化及主成分分析（PCA）处理后，评估各算法在精确率、召回率、F1分数和时间复杂度上的表现，以实现可靠实时检测与缓解。结果表明，随机森林、AdaBoost和XGBoost在准确率与效率上均优于其他算法，是实时应用的理想选择。
原文摘要 · Abstract (English)
Distributed Denial of Service attacks have become a significant threat to industries and governments leading to substantial financial losses. With the growing reliance on internet services, DDoS attacks can disrupt services by overwhelming servers with false traffic causing downtime and data breaches. Although various detection techniques exist, selecting an effective method remains challenging due to trade-offs between time efficiency and accuracy. This research focuses on developing an efficient real-time DDoS detection system using machine learning algorithms leveraging the UNB CICDDoS2019 dataset including various traffic features. The study aims to classify DDoS and non-DDoS traffic through various ML classifiers including Logistic Regression, K-Nearest Neighbors, Random Forest, Support Vector Machine, Naive Bayes. The dataset is preprocessed through data cleaning, standardization and feature selection techniques using Principal Component Analysis. The research explores the performance of these algorithms in terms of precision, recall and F1-score as well as time complexity to create a reliable system capable of real-time detection and mitigation of DDoS attacks. The findings indicate that RF, AdaBoost and XGBoost outperform other algorithms in accuracy and efficiency, making them ideal candidates for real-time applications.
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