A transfer hybrid deep learning approach for advanced intrusion detection in IoT-based smart home security

Abstract

As smart home environments increasingly rely on interconnected internet of things (IoT) devices, they face growing cyber threats originating both externally from malicious actors and internally from compromised or malfunctioning IoT devices. These threats, including unauthorized access, distributed denial of service (DDoS) attacks, and data exfiltration, pose significant risks to the security and privacy of smart home inhabitants. This paper introduces an advanced intrusion detection system (IDS) specifically designed to enhance the security of IoT-based smart home networks. Leveraging a hybrid deep learning approach combining convolutional neural networks (CNN) and long short-term memory (LSTM) models, complemented by transfer learning (TL) and hyper-parameter optimization techniques, our proposed IDS efficiently identifies both external and intra-network threats. Using the IoTID20 dataset, which simulates realistic attack scenarios, the IDS was trained and evaluated to detect abnormal behavior effectively within smart home networks. CNN layers extract spatial features from network traffic, while LSTM layers capture temporal dependencies, enabling robust detection against a range of cyber-threats. Evaluation results demonstrate the IDS’s high detection accuracy and exceptional F1-scores, validating its effectiveness in safeguarding IoT-based smart homes from evolving threats.