# IoTBystander: A Non-Intrusive Dual-Channel-Based Smart Home Security Monitoring Framework
## 3 Background: Emerging Multi-Heterogeneous-Platform Smart Homes  
The architecture of the heterogeneous multi-platform smart home system is illustrated in Figure 1. Within this system, various entities, including smart home IoT devices, hubs, platforms, and user terminals, interact with each other both in the physical environment and on the network to collectively fulfill the functions of the smart home. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Side Channel**} & \multicolumn{1}{c}{**Methodology**} & \multicolumn{1}{c}{**Application**} \\ \hline
[26] & Traffic & Association Rules Matching & Traffic Anomaly Detection \\ \hline
[31] & Traffic & CNN-based Framework & Traffic Anomaly Detection \\ \hline
[32] & Traffic & CNN + LSTM & Traffic Anomaly Detection \\ \hline
[33] & Traffic & Sensitive Information Inference & HTTPS and WPA/WPA2 Traffic Analysis \\ \hline
[38] & Audio & CNN-based Architectures & Audio Event Recognition/Detection \\ \hline
[41] & Audio & RCNN(RNN + CNN) & Audio Event Recognition \\ \hline
[42] & Audio & Vggish Model & Environmental Activity Recognition \\ \hline
[43] & Vision & KNN & Fall Detection in Elderly Individuals \\ \hline
[44] & Vision & Hidden Markov Model & Daily Activity Recognition for Elderly \\ \hline
[45] & Vision & Siamese Neural Network & IoT Device Anomaly Detection \\ \hline
[46] & Load & EMI Signature & Device Identification \\ \hline
[47] & Load & SVM & Power Status Monitoring (ON/OFF) \\ \hline
[48] & Load & Neural Networks + PSO & Device Identification/Monitoring \\ \hline
[52] & Traffic & Statistical Inference & Inferring user presence and activity \\ \hline
[53] & Traffic & Machine Learning & Identifying fine-grained user activities \\ \hline
[54] & Traffic & Empirical Analysis & Protecting User Privacy \\ \hline
[49] & Multiple Sensors & Multi-Sensor Fusion & Human Activity Recognition \\ \hline
[50] & Sensors+Traffic & Low/High-level Fusion Framework & Detection of Cyber–physical Anomaly \\ \hline
[51] & Optical Sensors & AI-driven Heterogeneous Sensing & Remote Monitoring in Smart Homes \\ \hline Ours & Audio+Traffic & Machine Learning+Rule Matching & Event Monitoring \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of side-channel-based monitoring. IoT Devices and Hubs. Smart devices in smart homes are classified into sensors and actuators. Sensors utilize specific sensing technologies to measure environmental attributes (e.g., temperature) and report the corresponding values. Actuators, such as smart door locks, receive and execute control _commands_, subsequently reporting their updated status. The new sensor measurements and actuator status are encapsulated in event notification messages (referred to as _events_) and transmitted to the smart home platform. An IoT hub device serves as a bridge to facilitate Internet access for smart devices using non-IP protocols. In scenarios where multiple smart home platforms are deployed, multiple hubs are installed to interconnect devices from different manufacturers. Platforms. Smart home platforms are generally categorized into cloud-based platforms and local platforms depending on their location of deployment. Cloud-based platforms are typically provided and maintained by smart device vendors or smart home system providers. Platforms offered by smart device vendors (i.e., vendor clouds) are often designed to be compatible only with devices produced by the same manufacturer. In contrast, the platforms provided by smart home system providers (i.e., integration platforms) are designed to promote greater interoperability, allowing the integration of devices from multiple brands and types. To achieve this, these platforms not only connect directly with devices or hubs but can also interface with platforms provided by device vendors. With user authorization, they can access devices connected to platforms from various manufacturers.