In the era of the Internet of Things (IoT), connected objects produce an enormous amount of data traffic that feed big data analytics, which could be used in discovering unseen patterns and identifying anomalous traffic. In this paper, we identify five key design principles that should be considered when developing a deep learning-based intrusion detection system (IDS) for the IoT. Based on these principles, we design and implement Temporal Convolution Neural Network (TCNN), a deep learning framework for intrusion detection systems in IoT, which combines Convolution Neural Network (CNN) with causal convolution. TCNN is combined with Synthetic Minority Oversampling Technique-Nominal Continuous (SMOTE-NC) to handle unbalanced dataset. It is also combined with efficient feature engineering techniques, which consist of feature space reduction and feature transformation. TCNN is evaluated on Bot-IoT dataset and compared with two common machine learning algorithms, i.e., Logistic Regression (LR) and Random Forest (RF), and two deep learning techniques, i.e., LSTM and CNN. Experimental results show that TCNN achieves a good trade-off between effectiveness and efficiency. It outperforms the state-of-the-art deep learning IDSs that are tested on Bot-IoT dataset and records an accuracy of 99.9986% for multiclass traffic detection, and shows a very close performance to CNN with respect to the training time.

中文翻译：

---

基于时间卷积神经网络和高效特征工程的物联网入侵检测系统

在物联网（IoT）时代，连接的对象会产生大量的数据流量，从而为大数据分析提供动力，可用于发现看不见的模式和识别异常流量。在本文中，我们确定了为物联网开发基于深度学习的入侵检测系统（IDS）时应考虑的五个关键设计原则。基于这些原则，我们设计并实现了时间卷积神经网络（TCNN），这是一种将物联网卷积神经网络（CNN）与因果卷积相结合的物联网入侵检测系统的深度学习框架。TCNN与综合少数群体过采样技术-名义连续（SMOTE-NC）相结合，以处理不平衡的数据集。它还与高效的要素工程技术相结合，其中包括特征空间缩减和特征转换。TCNN在Bot-IoT数据集上进行了评估，并与两种常见的机器学习算法（即Logistic回归（LR）和随机森林（RF））以及两种深度学习技术（即LSTM和CNN）进行了比较。实验结果表明，TCNN在有效性和效率之间取得了良好的折衷。它优于在Bot-IoT数据集上测试的最新深度学习IDS，并且记录了用于多类流量检测的99.9986％的准确性，并且在训练时间方面与CNN表现非常接近。实验结果表明，TCNN在有效性和效率之间取得了良好的折衷。它优于在Bot-IoT数据集上测试的最新深度学习IDS，并且记录了用于多类流量检测的99.9986％的准确性，并且在训练时间方面与CNN表现非常接近。实验结果表明，TCNN在有效性和效率之间取得了良好的折衷。它优于在Bot-IoT数据集上测试的最新深度学习IDS，并且记录了用于多类流量检测的99.9986％的准确性，并且在训练时间方面与CNN表现非常接近。