Summary of Large Language Models in Wireless Application Design: In-context Learning-enhanced Automatic Network Intrusion Detection, by Han Zhang et al.
Large Language Models in Wireless Application Design: In-Context Learning-enhanced Automatic Network Intrusion Detection
by Han Zhang, Akram Bin Sediq, Ali Afana, Melike Erol-Kantarci
First submitted to arxiv on: 17 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel framework that leverages large language models (LLMs), particularly generative pre-trained transformers (GPTs), to perform fully automatic network intrusion detection. The framework is empowered by three in-context learning methods, which are designed and compared to enhance the performance of LLMs. Experimental results on a real-world dataset demonstrate that in-context learning can significantly improve task processing performance without requiring further training or fine-tuning of LLMs. Specifically, testing accuracy and F1-Score can be boosted by 90% using GPT-4, while achieving an accuracy and F1-Score of over 95% on different types of attacks with only 10 in-context learning examples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses big language models to help detect problems in computer networks. They train the models to learn from examples and then test how well they do. The results are really good, with some models doing almost perfect! This is exciting because it could make it easier for computers to detect when someone is trying to hack into a network. |
Keywords
* Artificial intelligence * F1 score * Fine tuning * Gpt
