用LSTM预测未来DDoS攻击，助力提前防御
Forecasting Future DDoS Attacks Using Long Short Term Memory (LSTM) Model
- 基于LSTM模型分析网络流量趋势预测攻击
- 使用更新数据集提升预测准确率
- 适合网络安全防护与应急响应团队参考
本文利用深度学习模型预测未来的分布式拒绝服务（DDoS）攻击。尽管已有部分研究关注攻击预测，但相比以检测为主的文献仍显不足。通过分析当前攻击趋势并基于最新、更全面的数据集进行预测，可为制定和优化防御策略提供支持。研究采用跨行业数据挖掘标准流程（CRISP-DM）作为方法框架，确保预测过程系统化与可复现。
原文摘要 · Abstract (English)
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.
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