# LLM-Based Cyberattack Detection Using Network Flow Statistics
## 4 Related Work
### Other Strategies Non-LLM Based on ML  
Given there are few research papers related to our approach, we present some of the latest research results related to NIDS, which use different AI techniques. In order to analyse related works, we focus on strategies that make use of the selected dataset, so as to be able to compare the effectiveness of our proposal. [PERSON] et al. [5] published an interesting survey, which lists the most widely used techniques for each cybersecurity field. One of these is related to NIDS; they also introduced different AI techniques, none of which used, however, models based on NLP. Nonetheless, they mentioned BERT, in addition to GPT-3, but for SPAM detection. This survey references two interesting papers: [PERSON] et al. [12] proposed a Semi-Supervised DL approach for Intrusion Detection (SS-Deep-ID) with the following results for CSE-CIC-IDS2018: 98.71% accuracy, 94.91% precision, 94.30% recall, and 94.92% F-score. Their results were the following using the CIC-IDS-2017 dataset: 99.69% accuracy, 92.31% precision, 96.29% recall, and 94.18% F-score. They compared their results with other papers. With their proposal obtaining the best results for the metrics they selected. In the other cited paper, [PERSON] et al. [13] proposed a Generative Adversarial Network (GAN)-based approach, using the CSE-CIC-IDS2018 dataset. They reported a 95.32% accuracy, 99.88% precision, 95.85% recall, and 97.30% F-score. They also compared their results with different approaches found in other publications. [PERSON] et al. [22] proposed a Genetic Algorithm with a Back Propagation Neural Network (GA-BPNN) model for computer network safety. They applied their approach to the CIC-IDS-2017 datasets. Their results were 98% accuracy, 98.5% precision, 95% recall and 96.5% F-score. [PERSON] et al. [11] introduced an Intelligent Attention-Based Deep Convoluted Learning (IADCL) model. They applied their approach to different datasets, such as, NSL-KDD, CIC-IDS-2017, and CSE-CIC-IDS2018. They compared their results with other approaches they applied, including Immune Net, eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Decision Tree (DT), and Logistic Regression (LR). Considering the aforementioned techniques, that is, first their proposed approach and then the other approaches, their results when applying CSE-CIC-IDS2018 in terms of accuracy were 98.70%, 99.63%, 99.09%, 98.81%, 98.54%, and 92.96%; 99.72%, 99.64%, 99.03%, 98.82%, 93.41%, and 98.80% precision; 99.70%, 99.63%, 99.07%, 98.81%, 96.76%, and 90.96% recall; and an F-score of 99.71%, 99.63%, 99.07%, 98.81%, 95.06%, and 90.87%, respectively. Meanwhile, for CIC-IDS-2017, they reported 99.70%, 99.63%, 99.09%, 98.81%, 98.54%, and 92.96% accuracy; 99.72%, 99.64%, 99.03%, 98.82%, 93.41%, and 90.80% precision; 99.70%, 99.63%, 99.07%, 98.81%, 96.76%, and 90.96% recall; and an F-score of 99.71%, 99.63%, 99.07%, 98.81%, 95.06%, and 90.87%, respectively. [PERSON] et al. [23] introduced a 1D-TCN-ResNet-BiGRU-Multi-Head Attention (TRBMA)-based model, more concretely TRBMA (BS-OSS), a variant of the TRBMA model that integrates borderline SMOTE-OSS hybrid sampling, applying their approach to the CIC-IDS-2017 dataset. Their results were 99.88% accuracy, 99.86% precision, 99.88% recall, and 99.89% F-score. [PERSON] et al. [24] proposed a hybrid DL model for NIDS using Seq2 Seq and ConvLSTM-Subnets, applied to the CIC-IDS-2017 dataset. Their results were 99% accuracy, 97% precision, 96% recall, and 97% F-score. [PERSON] et al. [54] introduced an approach based on Augmented Wasserstein GAN (AWGAN) and Parallel Ensemble Learning-based Intrusion Detection (PEILID) algorithms, known as APEILID. They used the CSE-CIC-IDS2018 and NSL-KDD datasets for their study. Their results, after applying their approach with the first dataset, were 99.99% for all the metrics: accuracy, precision, recall, and F-score. This strategy is characterized by balancing the dataset using a GAN, generating synthetic data to improve training.