Internet of Things (IoT) allowed smart homes to improve the quality and the comfort of our daily lives. However, these conveniences introduced several security concerns that increase rapidly. IoT devices, smart home hubs, and gateway raise various security risks. The smart home gateways act as a centralized point of communication between the IoT devices, which can create a backdoor into network data for hackers. One of the common and effective ways to detect such attacks is intrusion detection in the network traffic. In this paper, we proposed an intrusion detection system (IDS) to detect anomalies in a smart home network using a bidirectional long short-term memory (BiLSTM) and convolutional neural network (CNN) hybrid model. The BiLSTM recurrent behavior provides the intrusion detection model to preserve the learned information through time, and the CNN extracts perfectly the data features. The proposed model can be applied to any smart home network gateway.

翻译：智能家庭网关是互联网设备之间的中央通信点,可为黑客创建网络数据后门。检测这类袭击的一个常见而有效的方法是网络交通中的入侵探测。在本文中,我们建议采用入侵探测系统(IDS)来检测智能家庭网络中的异常现象,使用双向短期内存(BILSTM)和脉动神经网络(CNN)混合模型。BILSTM的经常性行为提供了入侵探测模型,以便通过时间保存学到的信息,CNN完美地提取了数据特征。拟议的模型可以应用于任何智能家庭网络网关。