# Explainable Lightweight Block Attention Module Framework for Network-Based IoT Attack Detection
## 3 Proposed Methodology
### Data Augmentation Using a Self-Organizing Map Generative Adversarial Network  
While using a SOMGAN, self-organizing maps (SOM) can be used for feature extraction. A SOM is a type of unsupervised neural network that is used for dimensionality reduction and clustering of input data. It works by mapping high-dimensional data onto a low-dimensional grid and preserving the topological properties of the input space. The resulting map can be used to identify clusters in the data and reduce the dimensionality of the input space. The generator is then used to generate new samples that are similar to the input data but also diverse. Using a self-organizing map generative adversarial network (SOMGAN) for data augmentation in the context of identifying cyberattacks can offer several benefits, primarily related to improving the robustness and generalization of the cyberattack detection model. Here is why SOMGAN techniques might be used for data augmentation:  
1. Limited real data: In many cybersecurity applications, obtaining a diverse and extensive dataset of real-world cyberattacks can be challenging due to their infrequent occurrence or limited availability. Data augmentation techniques, like SOMGAN, can artificially expand the dataset, making the model more robust by exposing it to a wider range of possible attack scenarios.
2. Class imbalance: Cyberattack datasets often suffer from class imbalance, where certain attack types are rare compared to normal instances. This can lead to biased models that perform well on the majority class but poorly on the minority class (attacks). By generating synthetic attack instances, a SOMGAN can balance the class distribution and help the model better understand the characteristics of various attack types.
3. Generalization: Data augmentation helps the model generalize better. By exposing the model to a more diverse set of attack patterns, it learns to differentiate between normal and attack instances more effectively, even when faced with previously unseen or slightly different attack variations.
4. Anomaly detection: Many cyberattacks are \"anomalies\" compared to normal network behavior. Data augmentation techniques like SOMGAN can help the model learn to identify subtle anomalies that may not be well-represented in the original dataset.
5. Zero-day attacks: Data augmentation can aid in preparing the model for detecting zero-day attacks, which are previously unseen attack types. The model's exposure to a wider range of attack patterns through synthetic data can enhance its ability to identify novel attacks.
6. Improved feature learning: A SOMGAN can help the model learn more robust and relevant features from the data. This is particularly useful for complex and high-dimensional data like network traffic or system logs, where manual feature engineering can be challenging.
7. Reducing overfitting: By augmenting the dataset with synthetic data, the model is less likely to overfit to the limited real data. This is especially important when building deep learning models for cybersecurity, as overfitting can lead to poor generalization and a high false-positive/negative rate.  
In summary, SOM can be used as an alternative to other dense generative algorithms for data augmentation in a SOMGAN [38; 39; 40; 41; 42; 43]. A SOMGAN is sufficiently employed in a number of applications for data augmentation with unbalanced distributions in fault diagnosis, anomaly detection, and DoS attack detection.