标题： 使用长短期记忆（LSTM）模型预测未来的DDoS攻击 显示英文标题

标题： Forecasting Future DDoS Attacks Using Long Short Term Memory (LSTM) Model

摘要： 本文使用深度学习模型预测未来的分布式拒绝服务（DDoS）攻击。 尽管已有若干研究涉及DDoS攻击的预测，但相比以检测为重点的研究，这些研究仍相对有限。 通过研究当前趋势并基于更新的数据集进行预测，可以制定和形成针对这些攻击的缓解计划。 本研究工作采用的方法符合跨行业标准数据挖掘过程（CRISP-DM）模型。 显示英文摘要

摘要： This paper forecasts future Distributed Denial of Service (DDoS) attacks using deep learning models. Although several studies address forecasting DDoS attacks, they remain relatively limited compared to detection-focused research. By studying the current trends and forecasting based on newer and updated datasets, mitigation plans against the attacks can be planned and formulated. The methodology used in this research work conforms to the Cross Industry Standard Process for Data Mining (CRISP-DM) model.