# A Multimodal Framework for Advanced Cybersecurity Threat Detection Using GAN-Driven Data Synthesis
## 2 Related Works
The specific model also did not require large, labelled datasets, which are often required by supervised learning models. To overcome the issues of imbalanced datasets, which are often present in IoT applications, as well as the difficulties of contemporary NIDS in detecting previously unknown threats. [PERSON] et al. [22] proposed an algorithm that combined the strengths of GANs and Transformer models. More specifically, the so-called TransGAN-IDS model combined the generative capabilities of GANs and the ability of Transformers in capturing long-range dependencies in data. Experimental testing indicated an F1-score of 95.2% using the CIC-IDS2017 dataset [23] and of 83.2% using the Kitsune dataset [24]. [PERSON] and [PERSON] [25] proposed a model for IoT attack classification. This model made use of GANs for data augmentation as well as a Multilayer Perceptron classifier. The authors reported an F1-score of over 90% when using the BoT-IoT dataset [26] or the ToN-IoT [27] dataset. A model for IoV applications was showcased by [PERSON] et al. [28]. This model aimed at improving the detection of zero-day attacks in IoV applications, which often lack labelled data. The authors designed an attack sample augmentation algorithm, also incorporating a collaborative focal loss function into the discriminator to improve classification results. Experimental testing of the aforementioned approach indicated high F1 scores as compared to similar models, yielding an average 93.32% F1 score across four different attack types (i.e., DoS, Disruptive, RandomSpeed-RS, RandomPosOffset-RPO). The loss function used by the authors also outperformed other loss functions (Wasserstein Distance, Cross-Entropy Loss, Euclidean Distance, Kullback-Leibler Divergence) in terms of F1-score and AUC score. Another model for Intrusion Detection was proposed by [PERSON] and [PERSON] [29]. This model combined Wasserstein Conditional Generative Adversarial Networks (WCGANs) and an XGBoost classifier. It was used for both synthetic data generation and the classification of different kinds of attacks. It made use of a gradient penalty for updating weights and was experimentally tested on three datasets (i.e., BoT-IoT, UNSW-NB15, and NSL-KDD). Its performance was also compared to the DGM model [30], achieving better results in terms of Precision, Recall, and F1-scores (86.7%, 88.47%, and 87.58% respectively, as compared to 63.82%, 57.43%, and 60.46% of the DGM model). DDoS attacks and botnet detection are the main focal points in different GAN-based publications. [PERSON] et al. [31] proposed a GAN-based anomaly detection system for the detection and mitigation of DDoS attacks on software-defined networks. The model was tested with the Orion and the CIC-DDoS2019 datasets, yielding an F1-score of 98.5% and not being seriously affected by the imbalance in the datasets. The model also helped in the mitigation by determining which network flows will be included in a block list and which in a safe list. Botnet detectors often constitute targets of adversarial evasion attacks. Taking this into consideration, [PERSON] et al. [32] introduced a GAN model that also utilized deep reinforcement learning for both exploring semantically aware samples and hardening the detection of botnets. The so-called RELEVAGAN was also experimentally tested and compared to another similar model called EVAGAN [33]. The results indicated that RELEVAGAN outperformed EVAGAN in terms of convergence speed. More specifically, it converged at least 20 iterations before the EVAGAN in all the tested datasets. Aiming to address the issue of critical information leakage during the training of GANs for botnet detection, [PERSON] and [PERSON] [34] presented a method combining Deep Convolutional GANs (DCGANs) and Differential Privacy (DP). The authors used DCGANs to distinguish real and fake botnets, applied DP, and implemented a mix-up method for stabilizing the training process.