Overview

Overview

Malware Image-based Visualization

Datasets

Research in the field primarily focuses on a few datasets widely recognized as benchmarks, occasionally incorporating the original dataset to include more recent samples. The careful selection of datasets is essential as it lays the groundwork for rigorous and dependable research. The datasets frequently referenced in the literature are MalImg and Big2015. MalImg, introduced by Natraj et al. in 2011, is the oldest dataset in this context. MalImg This dataset comprises 25 imbalanced families, totaling 9,339 malware samples, all provided in grayscale image form.

Visualization

When generating the images for the dataset, researchers have to make important choices in two difficult areas: which type of available information to use as the basis to generate images and what technique, or techniques, to employ to generate those images. These two areas are largely independent but can be influence each other. Information used There are three distinct macro approaches to obtain information from a malware sample: static visualization, dynamic visualization, and hybrid visualization.

Feature Extraction

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Machine Learning Feature Extractors Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.

Classification Models

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Machine Learning Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.

After Classification

Whisper is a minimal documentation theme built for Hugo. The design & functionality is intentionally minimal. Adversarial Examples Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Interpretability Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.

Docs