A 2-Layers Deep learning Based Intrusion Detection System for Smart Home
Tahani Gazdar, Helah Alqarni, Aljazy Bakhsh, Mariam Aljidaani, Mashael Alzahrani · 2022
The future of smart homes is exciting. The number of smart devices connected to the Internet is supposed to increase from 31 billion in 2020 to 75.4 billion by 2025. These devices are increasingly vulnerable to cyberattacks because of the inherent connectivity in Smart Home. The attacker can use many techniques to compromise the system or cause damage to it such as ransomware, data and identity theft, DDoS attacks, etc. The preliminary results of the study show that people do not have the necessary level of culture to deal with attacks, also they are not aware of the potential security risks in their Smart Homes. They are not aware of the need for more tools to secure them. In light of this issue, this study propose a novel intrusion detection system for Smart Home environments. The proposed intrusion detection system will be based on two deep learning algorithms CNN and LSTM. To train and test the proposed model this study use a new dataset called TON-IoT specific to IoT environment and contains many records about many recent attacks in this particular network. The main goal of this study is to help the user monitors his Smart Home devices by detecting intrusions using a Deep Learning approach. Then, the proposed system will show to the user the detected intrusions through a dashboard. Upon the detection of an intrusion in a device, a notification will be shown to the user through the dashboard. Besides, the system will recommend some countermeasures to the user to help him harden his Smart Home and reduce some potential risks. The obtained results shows that the study model outperforms many existing models based on Machine learning algorithms.