论文标题
基于机器学习多层较高的DDOS攻击的预测方法
Prediction Approach against DDoS Attack based on Machine Learning Multiclassfier
论文作者
论文摘要
DDOS攻击（也称为分布式拒绝服务（DDOS）攻击）已成为互联网上最严重，最快的威胁之一。拒绝服务（DDOS）攻击是针对特定系统或网络的网络攻击的一个示例，试图在一段时间内使其无法访问或无法使用。结果，通过更好的算法和更高准确性的同时，改善各种类型的DDOS网络威胁的检测已成为检测DDOS网络威胁的最重要组成部分。为了适当捍卫目标网络或系统，首先确定已针对该的DDOS攻击的种类至关重要。本文介绍了许多合奏分类技术，其中结合了各种算法的性能。然后将它们与现有的机器学习算法进行比较，以使用精度，F1分数和ROC曲线检测不同类型的DDOS攻击方面的有效性。结果显示出很高的精度和良好的性能。
DDoS attacks, also known as distributed denial of service (DDoS) attacks, have emerged as one of the most serious and fastest-growing threats on the Internet. Denial-of-service (DDoS) attacks are an example of cyber attacks that target a specific system or network in an attempt to render it inaccessible or unusable for a period of time. As a result, improving the detection of diverse types of DDoS cyber threats with better algorithms and higher accuracy while keeping the computational cost under control has become the most significant component of detecting DDoS cyber threats. In order to properly defend the targeted network or system, it is critical to first determine the sort of DDoS assault that has been launched against it. A number of ensemble classification techniques are presented in this paper, which combines the performance of various algorithms. They are then compared to existing Machine Learning Algorithms in terms of their effectiveness in detecting different types of DDoS attacks using accuracy, F1 scores, and ROC curves. The results show high accuracy and good performance.
