# Intrusion Detection System in Smart Home Network Using Bidirectional LSTM and Convolutional Neural Networks Hybrid Model

arXiv: https://arxiv.org/abs/2105.12096 · HTML: https://mlsanity.com/paper/2105.12096/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 29 references that are in the map.
- First posted on arXiv 25 May 2021.

## Builds on (most cited first, 2 of 2)

- [Recent Advances in Recurrent Neural Networks](https://mlsanity.com/paper/1801.01078/) (2017)
- [Deep Gated Recurrent and Convolutional Network Hybrid Model for Univariate Time Series Classification](https://mlsanity.com/paper/1812.07683/) (2018)

## Built on by (most cited first, 3 of 3)

- [Review on Action Recognition for Accident Detection in Smart City Transportation Systems](https://mlsanity.com/paper/2208.09588/) (2022)
- [LiteLSTM Architecture Based on Weights Sharing for Recurrent Neural Networks](https://mlsanity.com/paper/2301.04794/) (2023)
- [Research on CNN-BiLSTM Network Traffic Anomaly Detection Model Based on MindSpore](https://mlsanity.com/paper/2504.21008/) (2025)

## Abstract (arXiv)

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.