A risk-aware and recommender distributed intrusion detection system for home robots

Abstract

The increasing adoption of home robots, driven by technological advancements and realistic goals, underscores the critical importance of ensuring their security, safety, and privacy. An Intrusion Detection System (IDS) is vital in safeguarding home robots by identifying and responding to potential security threats. However, home robot controllers’ limited computational and energy resources pose challenges. To enhance a home robot’s performance, especially its IDS, one potential solution is to connect it to a home network with a more robust computing infrastructure. Our proposed IDS comprises two components: one resides on a remote PC equipped with substantial computing power, and the other operates directly within the robot controller. By leveraging the remote PC, we achieve enhanced intrusion detection performance and gain valuable recommendations for fine-tuning the intrusion detection component situated in the controller. Simultaneously, it becomes imperative to swiftly identify security threats and prioritize high-risk attacks within the robot controller In this study, we employed machine learning techniques within the IDS. The dataset was generated by simulating robot actions within a home network. Based on our evaluation results, adopting a deep learning model for detecting intrusions in a remote PC and with its recommendations for fine-tuning the intrusion detector in the controller would significantly enhance intrusion detection performance. Additionally, leveraging ensemble learning to identify intrusions in the robot controller component while carefully considering potential risks promises satisfactory outcomes in terms of intrusion detection.