# Adaptive Semi-Supervised Algorithm for Intrusion Detection and Unknown Attack Identification
## 5 Conclusions  
In this paper, we propose a semi-supervised intrusion detection algorithm, TransGAN-IDS, which combines Transformer and GAN technologies. This algorithm aims to enhance intrusion detection capabilities in IoT environments by leveraging these advanced techniques to improve model accuracy and robustness when handling complex and high-dimensional data. TransGAN-IDS integrates the powerful feature extraction capabilities of Transformers with the generative abilities of GANs, thereby establishing a robust detection system. We employ various data preprocessing techniques, such as data cleaning and normalization, and transform the data into matrix features to optimize the model's training process and detection performance.  
Experimental results show that TransGAN-IDS excels across various performance metrics, particularly demonstrating outstanding capability in detecting rare or novel attacks, thus highlighting its potential value in IoT environments. Although TransGAN-IDS has achieved significant results, there is still room for further improvement. Future research will focus on several key areas: first, exploring more advanced Transformer architectures and GAN variants to enhance model performance and adaptability; second, investigating methods to strengthen model robustness through adversarial training, improving its defensive capabilities; and third, examining ways to design a lightweight version of TransGAN-IDS for deployment on devices with lower computational power.  
In addition to improving the detection capabilities of TransGAN-IDS, future work will also explore integrating complementary security measures, such as fuzz testing, to enhance overall IoT device security. Techniques like Snipuzz [31], which uses message snippet inference for fuzz testing IoT firmware, and mGPTFuzz [32], which leverages large language models for enhanced fuzzing of matter devices, could be combined with our intrusion detection system to identify potential vulnerabilities beyond network-based attacks. By incorporating such methods, we aim to build a more comprehensive defense strategy that addresses both known and unknown threats, thereby enhancing the robustness and adaptability of IoT security systems in the face of evolving cyber threats.  
Research on semi-supervised algorithm techniques, G.W.; research on Transformer algorithms, K.X. and M.L.; key algorithm analysis and design, M.L.; test verification, L.L. and K.X.; visualization, Y.W.; writing--review and editing, G.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Figure 14: Test results using different datasets in real-world in-vehicle scenarios.  
Data Availability Statement: The CIC-IDS2017 and Kitsune datasets used in this study are publicly available. The CIC-IDS2017 dataset can be accessed at [[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 24 June 2024), and the Kitsune dataset is available at [[https://archive.ics.uci.edu/dataset/516/kitsune+network+attack+dataset](https://archive.ics.uci.edu/dataset/516/kitsune+network+attack+dataset)]([https://archive.ics.uci.edu/dataset/516/kitsune+network+attack+dataset](https://archive.ics.uci.edu/dataset/516/kitsune+network+attack+dataset)) (accessed on 12 January 2025). The dataset used in collaboration with a car company is not publicly available due to commercial partnership.  
We would like to express our heartfelt thanks to anonymous reviewers and editors for their constructive comments on the paper.  
The authors declare no conflicts of interest.