As IoT networks become more complex and generate massive amounts of dynamic data, it is difficult to monitor and detect anomalies using traditional statistical methods and machine learning methods. Deep learning algorithms can process and learn from large amounts of data and can also be trained using unsupervised learning techniques, meaning they don't require labelled data to detect anomalies. This makes it possible to detect new and unknown anomalies that may not have been detected before. Also, deep learning algorithms can be automated and highly scalable; thereby, they can run continuously in the backend and make it achievable to monitor large IoT networks instantly. In this work, we conduct a literature review on the most recent works using deep learning techniques and implement a model using ensemble techniques on the KDD Cup 99 dataset. The experimental results showcase the impressive performance of our deep anomaly detection model, achieving an accuracy of over 98\%.

翻译：随着物联网网络日趋复杂并产生大量动态数据，传统统计方法和机器学习方法难以对其进行监控和异常检测。深度学习算法能够处理和学习海量数据，并可通过无监督学习技术进行训练，即无需依赖标注数据即可检测异常。这使得发现此前可能未被识别的新型未知异常成为可能。此外，深度学习算法具有自动化与高度可扩展性的特点，因此可在后台持续运行，实现对大规模物联网网络的实时监控。本研究对采用深度学习技术的最新成果进行了文献综述，并在KDD Cup 99数据集上利用集成方法构建了模型。实验结果表明，我们的深度异常检测模型性能出色，准确率超过98%。