## 5 Evaluation
### Discussion and Limitations  
Our research emphasizes the importance of choosing the right source domain for transfer learning, as this choice impacts learning performance. This perspective, which highlights the significance of selecting an appropriate source domain, has yet to be explored in existing research. The effectiveness of our framework is demonstrated through our evaluation results on the IoT Intrusion and Bot-IoT data set case studies. Remarkably, these data sets, sharing similar collection environments and attack labels compared to other data sets, exhibited the best accuracy as a source domain to each other when processed using our framework. This result highlights the framework's efficacy when collection environments and attack labels align closely.  
Compared to previous studies, our approach sets itself apart by not only focusing on the types and frequencies of attacks but also taking into account the environmental context of data collection based on the inherent similarity with the target domain. While existing approaches are primarily focused on the technical enhancement of trained intrusion detection models by utilizing diverse machine learning techniques, we recognize the importance of providing practical implications and insights for knowledge distillation. These insights are particularly valuable for practitioners and researchers in IoT security, who face challenges posed by diverse and heterogeneous IoT network environments. We believe the importance of our research lies not only in developing an effective intrusion detection model but also in emphasizing the crucial role of selecting an appropriate source domain based on its similarity to the target domain.  
However, our knowledge distillation strategy based on data set similarity would be inefficient in scenarios where the intersection across the source domain candidates and the target domain are limited and scarce. As our framework relies on a subset of shared features from varied candidate data set pools in the preprocessing phase, it might fail to accurately identify all the attacks specified within the data sets. We note that information loss due to the common feature extraction can be ameliorated if the raw packet data (e.g., PCAP files) are accessible. By utilizing the same flow analyzer (e.g., CICFlowMeter [34]) against candidate data set pools to acquire the flow information from the raw packet data, it is possible to fairly utilize all the flow features for transfer learning.  
If raw packet data are not provided, the number of common features could be drastically reduced when using datasets with vastly different characteristics, which could lead to performance degradation. However, these concerns can be alleviated primarily through screening by security operators before performing intrusion detection. For example, they can conduct qualitative analysis to ensure the collection environment is similar or the considered attack label is being targeted. Subsequently, they might encounter a situation in which it is hard to intuitively determine the most suitable source dataset among the filtered candidate data set pools. In such cases, we believe leveraging the proposed framework is considered best practice for comparing datasets and resolving uncertainties.  
In addition, the aforementioned limitation aligns with the inherent constraints of knowledge distillation based on data set similarity, particularly in scenarios involving the detection of unforeseen attack patterns (e.g., zero-day attacks). We believe such limitation can be addressed by leveraging the state-of-the-art ML techniques. For example, recent approaches that enhances the calibration of neural network confidence by leveraging outlier exposure [37] can be applicable to phase 2 in our framework to detect false alarms raised by zero-day attacks.