# A Multimodal Framework for Advanced Cybersecurity Threat Detection Using GAN-Driven Data Synthesis
## 3 Proposed Methodology
### Transfer Learning for Image-Based Detection  
The first branch of the proposed framework employs a transfer learning approach, consisting of an image-based threat detection mechanism centered around a pretrained ResNet50 CNN. This strategy capitalizes on the ability of deep residual networks to extract high-level visual features from structured image datasets, including those derived from \begin{table}
\begin{tabular}{c c c} \hline
**Attribute** & **Type** & **Description** \\ \hline Image Resolution & 64 \(\times\) 64 px & Image encoding format (based on original binary data) \\ Color Mode & Grayscale/RGB & Raw binary converted to pixel intensity values \\ Input Source Encoding & Byte stream & 0 = benign, 1 = malware \\ Label & Binary & e.g., Allaple.A, Lolyda.AA1, Wintrim.BX, etc. \\ Malware Families & Categorical & Random noise vector (e.g., 100 dimensions from Gaussian) \\ Generator Input (z) & Vector & e.g., 70\% train, 15\% val, 15\% test (combined real/synthetic) \\ Dataset Split & Percentage & Image encoding format (based on original binary data) \\ Image Resolution & 64 \(\times\) 64 px & Raw binary converted to pixel intensity values \\ \hline \end{tabular}
\end{table} 
Table 4: Characteristics of DCGAN-Generated Malware Image Data. \begin{table}
\begin{tabular}{c c c} \hline
**Aspect** & **ZDGAN for Zero-Day Attacks** & **ZDGAN for Botnet Intrusions** \\ \hline Source Dataset(s) & NSL-KDD, CTU-13 (general attacks) & CTU-13 (botnet-focused subsets) \\ Target Class Labels & 0 = malicious, 1 = benign & 0 = benign, 1 = botnet \\ Key Feature Types & Duration, Bytes, Protocol, State & Flow Duration, Packets, Botnet Family \\ Output Format & Tabular (network session records) & Tabular (flow-level intrusion logs) \\ Primary Use & Simulating rare or unknown attack types & Simulating diverse botnet behaviors \\ Integration Target & Ensemble classifier (Section 3.4) & Ensemble classifier (Section 3.4) \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of ZDGAN Model Applicability for Zero-Day vs. Botnet Synthetic Data Generation. malware binary samples. Taking the limited availability of labeled malware image datasets into account, transfer learning can be considered as a particularly effective solution, allowing the reuse of robust feature extractors trained on large-scale image datasets such as ImageNet. The ResNet50 architecture has been adjusted by freezing the initial convolutional layers and fine-tuning the final dense layers using a domain-specific dataset consisting of real and DCGAN-generated malware images (as described in Section 3.2 on 'DCGAN for Malware Image Synthesis'). The input malware images are uniformly resized to 64 \(\times\) 64 pixels and normalized to ensure compatibility with the pretrained layers. The training set has been split and balanced to include both benign and malicious samples, with augmentation further improving generalization. The model is trained using a binary cross-entropy loss function, the Adam optimizer with weight decay regularization, and a learning rate scheduler with early stopping based on validation loss. To make the model's decisions easier to understand, Gradient-weighted Class Activation Mapping (Grad-CAM) is optionally used to show which parts of the image influenced the prediction, supporting the classification decision-making. This helps to ensure that the model focuses on semantically meaningful visual features, rather than noise or artifacts introduced by the GAN generator. Evaluation metrics include classification accuracy, F1-score, false positive rate, and confusion matrix analysis. In previous experiments, incorporating DCGAN-generated malware samples during training led to a statistically significant improvement in the classifier's ability to generalize to previously unseen malware sets, especially those with obfuscated or polymorphic structures. The outputs of this second-layer branch are binary predictions denoting whether an input sample is classified as benign or malicious.