A Deep LSTM based Approach for Intrusion Detection IoT Devices Network in Smart Home

作者：Sylvia Worlali Azumah, Zag ElSayed, Victor Adewopo, Zaghloul Saad Zaghloul, Chengcheng Li · 年份：2021 · DOI：10.1109/wf-iot51360.2021.9596033 · 被引用次数：45 · 研究领域：Network Security and Intrusion Detection、Anomaly Detection Techniques and Applications、Advanced Malware Detection Techniques

The technological advancement of the Internet of Things (IoT) devices in our world today has become beneficial to many users but has security issues that are left unattended. IoT devices have the ability to connect to other devices on the internet to transmit and share data from anywhere. Hence, the need to secure these devices as they improve the quality of life comfort. Research shows that about 70% of IoT devices are easy to hack. Therefore, an efficient mechanism is highly needed to safeguard these devices, especially in smart homes. This paper proposes a novel deep learning-based anomaly detection approach to predict cyberattacks on smart home IoT network devices and learn new outliers as they occur over time using IoT network intrusion datasets. The proposed model is based on long-term memory architecture, which achieves a significant accuracy improvement compared to the existing state-of-the-art anomaly detection models for IoT networks in smart homes.