# Using Convolutional Neural Networks to Detect Compression Algorithms

arXiv: https://arxiv.org/abs/2111.09034 · HTML: https://mlsanity.com/paper/2111.09034/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 17 Nov 2021.

## Builds on (most cited first, 4 of 4)

- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Deep learning at the shallow end: Malware classification for non-domain experts](https://mlsanity.com/paper/1807.08265/) (2018)
- [Detecting Compressed Cleartext Traffic from Consumer Internet of Things Devices](https://mlsanity.com/paper/1805.02722/) (2018)
- [3D Conditional Generative Adversarial Networks to enable large-scale seismic image enhancement](https://mlsanity.com/paper/1911.06932/) (2019)

## Abstract (arXiv)

Machine learning is penetrating various domains virtually, thereby proliferating excellent results. It has also found an outlet in digital forensics, wherein it is becoming the prime driver of computational efficiency. A prominent feature that exhibits the effectiveness of ML algorithms is feature extraction that can be instrumental in the applications for digital forensics. Convolutional Neural Networks are further used to identify parts of the file. To this end, we observed that the literature does not include sufficient information about the identification of the algorithms used to compress file fragments. With this research, we attempt to address this gap as compression algorithms are beneficial in generating higher entropy comparatively as they make the data more compact. We used a base dataset, compressed every file with various algorithms, and designed a model based on that. The used model was accurately able to identify files compressed using compress, lzip and bzip2.