# Systematic Review: Malware Detection and Classification in Cybersecurity
## 2 Methodology
### Advanced Malware Detection Techniques
#### 3.6.8 Image-Based Malware Classification Using EfficientNet  
The article titled Image-based malware representation approach with EfficientNet convolutional neural networks for effective malware classification by [PERSON], [PERSON], and [PERSON], published in the _Journal of Information Security and Applications_, addresses malware classification using the EfficientNetB1 neural network and byte-level image representation techniques. Malware attacks have evolved significantly, becoming more sophisticated and evasive, emphasizing the need to develop advanced methods for their detection and classification.  
To mitigate the computational resources required for training and testing models based on convolutional neural networks (CNN), the authors evaluated the efficiency and performance of various pre-trained CNN models to select the best architecture for malware classification. In this context, pre-trained models were evaluated against different malware image representation methods, distinguished by image width size. The proposed EfficientNetB1 model achieved 99% accuracy in classifying malware classes from the Microsoft Malware Classification Challenge (MMCC) using fixed-width malware image representation, while requiring fewer network parameters than other pre-trained models.  
The proposed framework uses the EfficientNetB1 network for malware classification represented as images. Malware image representation converts malware files into RGB images, allowing the use of computer vision techniques for classification. Three different methods of malware image representation were performed to evaluate the performance of various CNN-based pre-trained models.  
EfficientNetB1 is part of the EfficientNet family, designed using compound optimization to adjust the model's depth, width, and resolution more efficiently. EfficientNetB1, in particular, offers an optimal balance between accuracy and computational efficiency.  
Malware files are converted into RGB images, where each byte of the file is translated into a pixel value. This approach leverages the capabilities of pre-trained CNNs in computer vision tasks to classify malware. The three malware image representation methods evaluated are: (1). Fixed-Width Images: Images are generated with a fixed width while maintaining the malware file's proportion. (2). Scaled Images: Images are scaled to have the same scale, allowing for uniform comparison. (3). Segmented Images: Malware files are segmented into smaller parts to generate multiple images.  
The EfficientNetB1 model was evaluated on the MMCC dataset, which contains 14,226 RGB images representing 25 malware classes and one benign class. The results showed that EfficientNetB1 outperformed other models in terms of accuracy and computational efficiency, achieving 99% accuracy in malware classification. Moreover, EfficientNet models required fewer network parameters compared to other pre-trained CNN models, making the classification process more efficient.  
The study demonstrates that combining malware image representation with EfficientNet networks is highly effective for malware classification. This approach not only improves detection accuracy but also significantly reduces the computational resources needed. The results validate that EfficientNetB1, with its optimized architecture, is a robust and efficient solution for malware classification using image representations [67].