The serving capabilities of networks are reduced by low-rate denial of service (LDoS) attacks that periodically send high-intensity pulse data flows. This type of attack shows a harmful effect similar to that of traditional DoS attacks, but their attack modes differ greatly. The high concealment of LDoS attacks makes it extremely difficult for traditional DoS detection methods to detect LDoS attacks. Meanwhile, the state-of-art detection methods for LDoS attacks have low-efficiency and resource-intensive and time complexity issues. We propose a novel detection method with analysis of abnormal network traffic under LDoS attacks that combines data mining technology. The judgement benchmarks were also established. The results from the experimental simulation on the simulated environment, physical environment and public datasets prove that the developed method can effectively detect LDoS attacks with optimal detection cost and low complexity, and has a high accuracy, a low false-negative rate, and a low false-positive rate.

定期发送高强度脉冲数据流的低速率拒绝服务 (LDoS) 攻击会降低网络的服务能力。这种类型的攻击表现出类似于传统DoS攻击的有害效果，但它们的攻击方式差异很大。LDoS 攻击的高度隐蔽性使得传统的 DoS 检测方法很难检测到 LDoS 攻击。同时，最先进的 LDoS 攻击检测方法存在效率低、资源密集和时间复杂的问题。我们提出了一种新的检测方法，结合数据挖掘技术，分析 LDoS 攻击下的异常网络流量。还建立了判断基准。模拟环境的实验模拟结果，