# Comparison of Deep Learning and the Classical Machine Learning Algorithm for the Malware Detection

arXiv: https://arxiv.org/abs/1809.05889 · HTML: https://mlsanity.com/paper/1809.05889/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2018.

## Built on by (most cited first, 5 of 5)

- [Robust Android Malware Detection System against Adversarial Attacks using Q-Learning](https://mlsanity.com/paper/2101.12031/) (2021)
- [Identification of Significant Permissions for Efficient Android Malware Detection](https://mlsanity.com/paper/2103.00643/) (2021)
- [Malware Classification Using Long Short-Term Memory Models](https://mlsanity.com/paper/2103.02746/) (2021)
- [DRLDO: A novel DRL based De-ObfuscationSystem for Defense against Metamorphic Malware](https://mlsanity.com/paper/2102.00898/) (2021)
- [ADVERSARIALuscator: An Adversarial-DRL Based Obfuscator and Metamorphic Malware SwarmGenerator](https://mlsanity.com/paper/2109.11542/) (2021)

## Abstract (arXiv)

Recently, Deep Learning has been showing promising results in various Artificial Intelligence applications like image recognition, natural language processing, language modeling, neural machine translation, etc. Although, in general, it is computationally more expensive as compared to classical machine learning techniques, their results are found to be more effective in some cases. Therefore, in this paper, we investigated and compared one of the Deep Learning Architecture called Deep Neural Network (DNN) with the classical Random Forest (RF) machine learning algorithm for the malware classification. We studied the performance of the classical RF and DNN with 2, 4 & 7 layers architectures with the four different feature sets, and found that irrespective of the features inputs, the classical RF accuracy outperforms the DNN.