## 2 Related Work
### Transfer Learning  
Transfer learning [16] is a machine learning paradigm encompassing the utilization of acquired knowledge from a source model to enhance the performance on another distinct yet interconnected target model. It empowers models to extract broader insights from previously assimilated features to address restricted data availability and computational constraints. The utility of transfer learning spans diverse domains, including but not limited to computer vision [22; 23], natural language processing [24; 25], and anomaly detection [26; 27], where pre-trained models can be fine-tuned for specific tasks. Transfer learning has demonstrated significant success in cases where labeled data for a target task are limited [28], making it an essential technique in modern machine learning. Figure 1 illustrates the comparison between transfer learning and traditional machine learning.  
[PERSON] et al. (2017) proposed a novel Deep Transition Learning (DTL) method to learn from data collected from multiple unlabeled IoT devices. Labeled and unlabeled samples were trained on a data set containing both source and target domains, respectively, and after training, MMD-AE was used to predict IoT attacks in incoming traffic in the target domain. MMD-AE is divided into AE1 and AE2, and it uses the Maximum Mean Mismatch (MMD) metric to minimize the distance between AE1's multiple hidden layers and AE2's multiple hidden layers. MMD has helped improve the effectiveness of knowledge transmitted from source to target domains in IoT attack detection systems.  
To simulate a zero-day attack scenario, a study by [PERSON] et al. (2018) evaluated the performance of well-detecting attacks that were not present in the training phase when tested. Based on the concept of transfer learning, a new neural network was designed through the fine-tuning of parameters in the original deep neural network. The aim was to enhance performance by leveraging transfer learning to create a new deep neural network through parameter fine-tuning in existing networks.  
A study by [PERSON] et al. (2019) proposed an approach to distribute DNN training across IoT edge devices based on transfer learning. Delegating DNN training onto edge devices from the central cloud reduces communication costs and preserves data privacy on edge devices. The authors demonstrated that the proposed scheme only incurs a 3.5% loss of accuracy compared to the conventional DNN training conducted on the cloud.  
The primary objective of our framework is to identify a suitable source domain data set from among potential candidates, which is important in the context of IoT ecosystems due to its diverse and heterogeneous nature. For a modeler aiming to construct an intrusion detection classifier, computational resources may be limited, making it essential to prioritize the selection of an appropriate source domain for implementing transfer learning. It is noteworthy that existing approaches fall short in providing a definitive answer to this challenge, whereas our framework excels in this regard. Our framework embodies a complete process, starting from a proper source data set selection appropriate for the target domain to conventional instance-based transfer learning. We note that the state-of-the-art transfer learning techniques ([PERSON] et al., 2017) and improved models ([PERSON] et al., 2017; [PERSON] et al., 2018) are applicable in the transfer learning phase of our proposed framework (see Section 4.3). In summary, our ultimate goal is to surpass the mere utilization of transfer learning and strive for efficient adaptation to new IoT environments.