# LLM-Based Cyberattack Detection Using Network Flow Statistics
## 4 Related Work
### Other Strategies Non-LLM Based on ML
This means that we cannot compare our results with those published in this paper since we do not use the same data to train our model. [PERSON] et al. [55] proposed Chaotic Zebra Optimization Algorithm and Long Short-Term Memory (CZOLSTM) for the detection of cyberthreats. They used the CSE-CIC-IDS2018 dataset and their results were 99.92% accuracy and 99.94% precision, recall and F-score. They made a binary classification between benign and malignant data, and, in addition to taking just malignant data, developed a classification from a subset of types of attacks contained in the original dataset. Therefore, our results cannot be compared with theirs. [PERSON] et al. [14] proposed a hybrid DL model for an IDS on the IoT platform. For this study, they used the Bot-IoT and CSE-CIC-IDS2018 datasets. For their proposal, and using the latter dataset, their results were 99.17% accuracy, 99.26% precision, 99.17% recall, and 99.2% F-score. [PERSON] et al. [15] proposed a framework based on a Convolutional Neural Network (CNN). They used different datasets, such as CSE-CIC-IDS2018, UNSW-NB15, and CIC-Darknet2020. For the first dataset, they obtained a 99.4% of accuracy, using their proposed framework. [PERSON] et al. [16] presented an Attention-based CNN for IDS. They carried out their research using CSE-CIC-IDS2018. For their proposed model, they obtained 99.36% accuracy. They reported no results on other metrics. [PERSON] et al. [17] proposed a novel idea of hybrid meta-DL to detect malicious packet data. They used a combination of Siamese and Prototypical networks, where the first was used for binary classification and the latter for multiclass classification. For their hybrid meta-approach, using the CSE-CIC-IDS2018 dataset, their results were 90.64% accuracy, 91.66% precision, 90.64% recall and 91% F-score. Additionally, using CIC-IDS-2017, their results were 95.68% accuracy, 96.50% precision, 95.68% recall and 96.10% F-score. [PERSON] et al. [18] proposed the Hierarchical and Dynamic Feature Extraction Framework (HDFEF) for IDS. They presented results for different datasets as CIC-IDS-2017, UNSW-NB15, and CSE-CIC-IDS2018. For the first dataset, they obtained 99.73% precision, 99.96% recall and 99.84% F-score. For the latter dataset, they obtained 98.22% precision, 99.77% recall and 98.49% F-score. They reported no results about accuracy. [PERSON] et al. [19] used different approaches for NIDS, including RF, CatBoost, LightGBM and XGBoost, applied to different datasets, such as CIC-IDS-2017, CSE-CIC-IDS2018, and INSDN. Regarding their results with the CSE-CIC-IDS2018 dataset, using RF, they obtained 93% precision, 71% recall, and 74% F-score; using CatBoost, they obtained 89%, 83%, and 83%, respectively; and using LightGBM they obtained 87%, 84%, and 85%, respectively. Finally, with XGBoost, they obtained 89%, 85%, and 86% respectively. They presented no results related to accuracy. For CIC-IDS-2017, using RF, they obtained 93% precision, 71% recall, and 74% F-score; using CatBoost, they obtained 89%, 83%, and 83%;using LightGBM, they obtained 87%, 84%, and 85%; and using XGBoost, they obtained 89%, 85%, and 86%, all respectively. No results were reported for accuracy. [PERSON] et al. [20] proposed a classifier using an approach called Multinomial Mixture Modeling with Median Absolute Deviation and RF (MMM-RF). They used the CSE-CIC-IDS2018 dataset and obtained 99.98% of accuracy with their approach. They presented no results for other metrics. [PERSON] et al. [21] presented a Packet Bytes-based CNN (PBCNN) for NIDS. They used CSE-CIC-IDS2018, with their results being 99.99% accuracy, 98.2% precision, and 98.3% recall and F-score.