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Abstract：
The proliferation of Internet of Things (IoT) devices and fog computing architectures has introduced major security and cyber threats. Intrusion detection systems have become effective in monitoring network traffic and activities to identify anomalies that are indicative of attacks. However, constraints such as limited computing resources at fog nodes render conventional intrusion detection techniques impractical. This paper proposes a novel framework that integrates stacked autoencoders, CatBoost, and an optimised transformer-CNN-LSTM ensemble tailored for intrusion detection in fog and IoT networks. Autoencoders extract robust features from high-dimensional traffic data while reducing the dimensionality of the efficiency at fog nodes. CatBoost refines features through predictive selection. The ensemble model combines self-attention, convolutions, and recurrence for comprehensive traffic analysis in the cloud. Evaluations of the NSL-KDD, UNSW-NB15, and AWID benchmarks demonstrate an accuracy of over 99% in detecting threats across traditional, hybrid enterprises and wireless environments. Integrated edge preprocessing and cloud-based ensemble learning pipelines enable efficient and accurate anomaly detection. The results highlight the viability of securing real-world fog and the IoT infrastructure against continuously evolving cyber-attacks.
摘要：
物联网(IoT)设备和雾计算架构的激增引入了主要的安全和网络威胁。入侵检测系统在监视网络流量和活动以识别指示攻击的异常方面已变得有效。然而,诸如雾节点处的有限计算资源之类的限制使得传统的入侵检测技术不切实际。本文提出了一种新颖的框架，集成了堆叠式自动编码器，CatBoost,以及为雾和物联网网络中的入侵检测量身定制的优化变压器-CNN-LSTM集成。自编码器从高维交通数据中提取鲁棒特征，同时降低雾节点效率的维度。CatBoost通过预测性选择来完善功能。合奏模型结合了自我注意力，卷积，和复发，以便在云中进行全面的流量分析。对NSL-KDD的评估，UNSW-NB15和AWID基准测试表明，在检测传统威胁方面，准确率超过99%，混合企业和无线环境。集成的边缘预处理和基于云的集成学习管道可实现高效准确的异常检测。结果强调了保护现实世界的雾和物联网基础设施免受不断发展的网络攻击的可行性。
