# A Multimodal Framework for Advanced Cybersecurity Threat Detection Using GAN-Driven Data Synthesis
## 2 Related Works
Experimental testing of the method indicated a classification accuracy of 87.4% while keeping information leakage during the training process at acceptable levels. In the following publications, ensemble machine learning techniques were applied for botnet detection. [PERSON] et al. [35] combined three ML models (i.e., Random Forest--RF, Generalized Linear Model--GLM, and Decision Trees--DT), building a stacking ensemble model for detecting botnets in computer traffic. Out of the three individual ML models, the RF yielded the best coefficient of determination (R\({}^{2}\)), reaching 0.9977, followed by the DT with 0.9882, and the GLM with 0.9522. The use of the stacking ensemble model led to an increase in the R\({}^{2}\) as compared to the use of individual ML models, resulting in a 0.2% improvement compared to RF and 1.15% and 3.75% as compared to the DT model and the GLM model, respectively. Another model based on ensemble learning was proposed byAbu [PERSON] and [PERSON] [36].The so-called ELBA-IoT model aimed to serve as a lightweight botnet detection system for IoT applications. More specifically, the authors combined three DT techniques (i.e., AdaBoosted, RUSBoosted, Bagged). The proposed model was capable of profiling behavioral features in IoT networks and detecting anomalous traffic from compromised IoT nodes. Experimental testing showed that ELBA-IoT could reach high accuracy rates (up to 99.6%) while having a very low inference time of 40 \(\upmu\)s. The accuracy of the ensemble classifier was higher than the accuracy of each individual classifier (AdaBoosted reached 97.3%, RUSBoosted reached 97.7%, and Bagged reached 96.2%). [PERSON] and [PERSON] [37] presented a model for botnet detection that used ensemble learning and combined different feature selection techniques. More specifically, for the feature selection, the Principal Component Analysis, Mutual Information, and Categorical Analysis techniques were combined. Furthermore, the Extra Trees ensemble classification models were used, in which every decision tree was trained on a random subset of features from the input dataset. Experimental testing using different datasets (e.g., N-BaIoT, Bot-IoT, CTU-13, ISCX, CCC, CICIDS) indicated very high performance in terms of botnet detection, reaching a true positive rate of 99%. Finally, [PERSON] and [PERSON] [38] presented an ensemble classifier for botnet detection with a stacking process called ECASP. The proposed model yielded an accuracy of 94.08%, a sensitivity of 86.5%, and a specificity of 85.68% when tested on publicly available datasets. ECASP outperformed three different models, i.e., an Extreme Learning Machine (ELM), a Support Vector Machine (SVM), and a CNN model. While there is significant research interest around GAN-based models for detecting network attacks, most publications focus on specific attack types or on specific data modalities. On the contrary, the work presented in this paper follows an integrative and scalable approach that encompasses diverse attack types and data modalities. As a result, it can handle both tabular data and images, and detect zero-day attacks, botnet attacks, as well as malware hidden within image files.