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.

翻译：物联网（IoT）系统中日益增长的安全漏洞要求改进威胁检测方法。本文提出了一种紧凑高效的方法来检测僵尸网络攻击，该方法采用了一种集成方案，包含流量模式分析、时序支持学习和聚焦特征提取。所提出的基于注意力的模型受益于混合CNN-BiLSTM架构，利用N-BaIoT数据集在检测僵尸网络攻击时实现了99%的分类准确率，并在多种场景下保持了高精确率与召回率。该模型的性能进一步通过马修斯相关系数和科恩卡帕相关系数等关键参数得到验证。这些参数接近理想值的结果表明，所提模型能够在实际场景中对未见数据准确高效地检测僵尸网络攻击。该模型被证明是物联网网络应对新兴安全挑战的有力防御机制。