# Deep learning at the shallow end: Malware classification for non-domain experts

arXiv: https://arxiv.org/abs/1807.08265 · HTML: https://mlsanity.com/paper/1807.08265/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2018.

## Built on by (most cited first, 11 of 11)

- [Malware Classification Using Deep Boosted Learning](https://mlsanity.com/paper/2107.04008/) (2021)
- [Adversarial Scratches: Deployable Attacks to CNN Classifiers](https://mlsanity.com/paper/2204.09397/) (2022)
- [A Survey of Deep Learning Models for Structural Code Understanding](https://mlsanity.com/paper/2205.01293/) (2022)
- [Avast-CTU Public CAPE Dataset](https://mlsanity.com/paper/2209.03188/) (2022)
- [Echelon: Two-Tier Malware Detection for Raw Executables to Reduce False Alarms](https://mlsanity.com/paper/2101.01015/) (2021)
- [Deep Image: A precious image based deep learning method for online malware detection in IoT Environment](https://mlsanity.com/paper/2204.01690/) (2022)
- [On deceiving malware classification with section injection](https://mlsanity.com/paper/2208.06092/) (2022)
- [New Approach to Malware Detection Using Optimized Convolutional Neural Network](https://mlsanity.com/paper/2301.11161/) (2023)
- [Using Convolutional Neural Networks to Detect Compression Algorithms](https://mlsanity.com/paper/2111.09034/) (2021)
- [Sandbox Sample Classification Using Behavioral Indicators of Compromise](https://mlsanity.com/paper/2201.07359/) (2022)
- [Bytes to Threats: Byte-Sequence Transformer For Malware Detection](https://mlsanity.com/paper/2610.18787/) (2026)

## Abstract (arXiv)

Current malware detection and classification approaches generally rely on time consuming and knowledge intensive processes to extract patterns (signatures) and behaviors from malware, which are then used for identification. Moreover, these signatures are often limited to local, contiguous sequences within the data whilst ignoring their context in relation to each other and throughout the malware file as a whole. We present a Deep Learning based malware classification approach that requires no expert domain knowledge and is based on a purely data driven approach for complex pattern and feature identification.