# Image-Based Malware Detection Using \(\alpha\)-Cuts and Binary Visualisation
## 5 Discussion
Surprisingly, the experiment of our proposed method, whose 10,000 image dataset was reduced by \(\alpha=0.6\), and the experiment of [17]'s method, whose 10,000 image dataset was reduced by \(\alpha=0.3\) provided solid indications for robust predictions: both models' curves exhibited steady increases for training and validation accuracy and steady decreases for training and validation loss, with the first model being the closest to the ideal. This is consistent with the literature cited by [102; 103], who emphasise the effectiveness of an increasing dataset size even over algorithm selection. At the same time, given the curves' fluctuation, for the remaining experiments of [17]'s approach (Tables 5 and 7) and ours (Tables 7-9), a dataset of 10,000 images did not make any contributions towards model robustness. We should also note that the experiment with the approach of [18] for the 10,000 image dataset displayed good learning curves too. However, we had to reject it altogether due to lower accuracy scores. Given the robustness of the ResNet50 model that incorporated our proposed colouring method and was processed with an \(\alpha\)-cut of 0.6 (for a fuzzy set of increasing linear shape), we are confident that with a larger dataset (of more than 10,000 images) and enough training, our evaluation metrics would report greater scores (>93.6% accuracy and >93.53% f-score). Undoubtedly, and despite the restrictions we imposed in our method, for the dataset of 10,000 images, \(\alpha\)-cuts provided model robustness where unprocessed datasets failed to do so. From the above findings, we can safely conclude that \(\alpha\)-cuts can be regarded as a valuable image processing method. As we observed, they not only contribute to matrix sparsity but can also assist in demanding image datasets of synthetic representations (as opposed to natural images, e.g., of objects, animals, etc.). At this point, it is important to note that each type of dataset requires experimentation with the value \(\alpha\), fuzzy set shape, and dataset size to decide on the appropriate level of processing. As mentioned earlier in this paper, a key strength of this processing method is that it is colour model-independent. \(\alpha\)-cuts can be applied to any type of image, as long as the colour model values defining each pixel are well-defined fuzzy sets. This is particularly important since there is a plethora of popular colour models involved in image processing. At the same time, several image segmentation methods are not as simple or possess disadvantages. For example, although the RGB model seems to serve image processing well, there are still major critiques of its application in colour segmentation [64; 104; 105]. Another important but not as straightforward contribution of this method is that reducing an image into a matrix of 0 s and 1 s can act as an extra layer of data anonymisation for the already converted image. This enhances compliance with GDPR's \"privacy by design policy\", which requests that user data be processed to a satisfactory state of anonymity as mentioned in Section 1. Despite the novelty of these findings, a number of limitations need to be noted. First, we were only able to test our datasets for a limited number of \(\alpha\)-cuts. Our initial thoughts included testing for \(\alpha=\{0.1,0.2,\ldots,0.9\}\); however, computational time and costs limited our experiments to two \(\alpha\)-cuts. However, it would be interesting to observe how different \(\alpha\)-cuts and large enough datasets contribute to model effectiveness. Second, we arbitrarily defined the red, green, and blue channels as linear-shaped fuzzy sets. This rather simple shape served as a straightforward method where we could easily observe the relationship between value \(\alpha\) and visual colour change in images. More specifically, the higher the value of \(\alpha\), the more 0 s the processed image would have (\(\alpha A\) would become smaller). Hence, for an increasing \(\alpha\), images approached black.