# Application Layer Protocol Identification Method Based on ResNet
## 5 Conclusions  
This paper first underlined the importance of protocol recognition technology. It then elaborated on three main methods in the field of protocol recognition: DPI, protocol feature matching recognition, and deep learning recognition, comparing the advantages and disadvantages of each method. It highlighted the key issue of correctly and efficiently extracting protocol features. Two preprocessing algorithms were detailed: the separator determination algorithm based on information entropy and the RGB image generation algorithm based on composite features. Subsequently, a protocol recognition algorithm based on ResNet was proposed. Finally, the hardware and software information of the test environment, the datasets used, and the deep learning models were introduced.  
In this environment, experiments were conducted first on the separator determination algorithm based on information entropy, analyzing the separation effects of different separators. Subsequently, a comparative experiment was set up with three groups of different separators, and three models were trained and tested using five different input datasets. The experimental results showed that all four metrics improved by more than 2%, exceeding 98%, demonstrating the effectiveness and accuracy of this method. Furthermore, comparing the training process with grayscale images under the same conditions proved that this method reduced the risk of overfitting during training. Under acceptable model performance loss, the proposed method in this paper is deemed the optimal solution.  
Our method demonstrates high accuracy in recognizing application-layer protocols, which is significant for network security and traffic management. However, there are still limitations and room for improvement in our method. For example, in the recognition of encrypted data in application-layer protocol recognition, the feature separation may not be accurate enough. We plan to further optimize the delimiter determination algorithm and feature extraction process, prepare to apply the method proposed in this article to encryption protocols, improve the recognition process of encrypted data, improve the accuracy and speed of encrypted data feature extraction, and use more pre-trained models and datasets for verification. Additionally, we can consider using more advanced deep learning models or other machine learning methods to improve recognition performance further.  
Conceptualization, H.Z.; funding acquisition, Z.F., H.Z., and Q.G.; methodology, H.Z.; software, X.G.; validation, X.G.; writing\(-\)original draft, X.G.; writing\(-\)review and editing, Z.F., X.G., H.Z., J.T. and Q.G. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Zhejiang Provincial Department of Science and Technology's \"Elite\" R&D Program Project (2024C01019), the Key Research and Development Program of Zhejiang Province under Grant (2024C01108), and the Zhejiang Provincial Natural Science Foundation of China (LQ20F020003).  
CIC-IDS2017 data set: [[https://www.unb.ca/cic/datasets/ids-2017.html](https://www.unb.ca/cic/datasets/ids-2017.html)]([https://www.unb.ca/cic/datasets/ids-2017.html](https://www.unb.ca/cic/datasets/ids-2017.html)), accessed on 23 December 2024; Shodan data set: www.shodan.io, accessed on 23 December 2024.  
The authors declare no conflict of interest.