用GRU与神经图灵机结合，精准识别网络中的拒绝服务攻击
Intelligent DoS and DDoS Detection: A Hybrid GRU-NTM Approach to Network Security
- 用GRU处理数据流时序特征，用NTM捕捉长期攻击模式
- 在UNSW-NB15和BoT-IoT数据集上达到99%检测准确率
- 适合需要实时防护的网络系统安全团队使用
检测拒绝服务（DoS）和分布式拒绝服务（DDoS）攻击仍是网络安全中的关键挑战。本文提出一种融合门控循环单元（GRU）与神经图灵机（NTM）的混合深度学习模型，用于提升入侵检测能力。模型在UNSW-NB15和BoT-IoT数据集上训练，利用GRU层处理序列数据，通过NTM实现对长期攻击模式的识别。实验结果显示，该方法在区分正常流量、DoS和DDoS攻击时达到99%的准确率。研究成果为实时威胁检测提供了有效方案，有助于提升各类场景下的网络安全性。
原文摘要 · Abstract (English)
Detecting Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks remains a critical challenge in cybersecurity. This research introduces a hybrid deep learning model combining Gated Recurrent Units (GRUs) and a Neural Turing Machine (NTM) for enhanced intrusion detection. Trained on the UNSW-NB15 and BoT-IoT datasets, the model employs GRU layers for sequential data processing and an NTM for long-term pattern recognition. The proposed approach achieves 99% accuracy in distinguishing between normal, DoS, and DDoS traffic. These findings offer promising advancements in real-time threat detection and contribute to improved network security across various domains.
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