# IoTBystander: A Non-Intrusive Dual-Channel-Based Smart Home Security Monitoring Framework
## 9 Conclusions  
In this research, we have conducted an exploration of the potential of leveraging two side channels: audio and network traffic, to develop a non-intrusive monitoring framework, IoTBystander, to improve the comprehensiveness and resilience of smart home security monitoring. Our experimental evaluations in a real-world context have demonstrated that IoTBystander is capable of achieving satisfactory performance in the detection and recognition of smart home events without the need to modify the existing infrastructure. This approach underscores the framework's ability to provide a versatile and robust solution for smart home monitoring, aligning with the exigencies of modern security demands.  
However, like any system, IoTBystander has limitations. One of the key challenges is the reliance on labeled data for training, which necessitates manual supervision. The quality of the labeled data directly impacts the system performance, and incomplete or inaccurate labeling can result in misclassifications. Moreover, this dependency on labeled datasets presents a significant barrier to real-world deployment, where such datasets may not always be readily available. To mitigate this, future research could explore automatingthe labeling process or employing semi-supervised learning techniques to reduce manual effort and improve the scalability of the approach.  
While IoTBystander offers significant improvements in monitoring, it is essential to acknowledge that each channel has inherent strengths and weaknesses. For example, the audio channel may miss events that do not produce audible sounds, while network traffic analysis cannot capture events from non-smart devices. Therefore, expanding the system to integrate additional side channels is critical to achieving a more holistic view of smart home activities. Our future work will focus on identifying viable side channels and designing effective collaborative monitoring strategies to further enhance the robustness and comprehensiveness of the IoTBystander framework.  
Conceptualization, H.C.; Formal analysis, H.C.; Funding acquisition, H.C., Y.W. and J.Y.; Investigation, H.C., J.Y. and H.G.; Methodology, H.C. and Q.M.; Project administration, H.C., Y.W. and H.G.; Software, Q.M.; Supervision, H.G.; Validation, H.C., Y.W. and J.Y.; Visualization, Q.M.; Writing--original draft, H.C. and Q.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China (NSFC) under Grant No. 62302282 and 62472267, the Shanxi Province Science Foundation under Grant No. 202203021222005, 20203021222010 and 202403021212171, and Research Project Supported by Shanxi Scholarship Council of China under Grant No. 2024-018 and 2024-019.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the risks of sensitive information leakage.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.