  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.
