用改进的注意力CNN-BiLSTM模型，高效检测物联网中的僵尸网络攻击。
Efficient IoT Intrusion Detection with an Improved Attention-Based CNN-BiLSTM Architecture
- 融合流量模式分析与时间支持学习，通过注意力机制聚焦关键特征。
- 在N-BaIoT数据集上达到99%准确率，各类场景下精度和召回率均高。
- 适合需要轻量级、高可靠性的物联网安全防护系统使用。
日益增长的物联网（IoT）系统安全漏洞亟需更有效的威胁检测方法。本文提出一种紧凑高效的检测方案，通过整合流量模式分析、时间支持学习与聚焦特征提取，实现对僵尸网络攻击的有效识别。所提基于注意力机制的混合CNN-BiLSTM模型在N-BaIoT数据集上取得99%的分类准确率，且在多种场景下保持高精度与高召回率。通过马修斯相关系数（Mathews Correlation Coefficient）与科恩卡帕相关系数（Cohen's kappa Correlation Coefficient）等关键指标验证，结果接近理想，表明该模型在实际部署及未见数据上均具备精准高效的攻击检测能力。该模型可作为应对新兴物联网安全挑战的强大防御机制。
原文摘要 · Abstract (English)
The ever-increasing security vulnerabilities in the Internet-of-Things (IoT) systems require improved threat detection approaches. This paper presents a compact and efficient approach to detect botnet attacks by employing an integrated approach that consists of traffic pattern analysis, temporal support learning, and focused feature extraction. The proposed attention-based model benefits from a hybrid CNN-BiLSTM architecture and achieves 99% classification accuracy in detecting botnet attacks utilizing the N-BaIoT dataset, while maintaining high precision and recall across various scenarios. The proposed model's performance is further validated by key parameters, such as Mathews Correlation Coefficient and Cohen's kappa Correlation Coefficient. The close-to-ideal results for these parameters demonstrate the proposed model's ability to detect botnet attacks accurately and efficiently in practical settings and on unseen data. The proposed model proved to be a powerful defense mechanism for IoT networks to face emerging security challenges.
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