# Malware Detection using Machine Learning and Deep Learning

arXiv: https://arxiv.org/abs/1904.02441 · HTML: https://mlsanity.com/paper/1904.02441/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2019.

## Built on by (most cited first, 9 of 9)

- [Robust Android Malware Detection System against Adversarial Attacks using Q-Learning](https://mlsanity.com/paper/2101.12031/) (2021)
- [Multi-SpacePhish: Extending the Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learning](https://mlsanity.com/paper/2210.13660/) (2022)
- [Deep Reinforcement Learning for Cybersecurity Threat Detection and Protection: A Review](https://mlsanity.com/paper/2206.02733/) (2022)
- [DRLDO: A novel DRL based De-ObfuscationSystem for Defense against Metamorphic Malware](https://mlsanity.com/paper/2102.00898/) (2021)
- [Demand Forecasting for Platelet Usage: from Univariate Time Series to Multivariate Models](https://mlsanity.com/paper/2101.02305/) (2021)
- [Improving type information inferred by decompilers with supervised machine learning](https://mlsanity.com/paper/2101.08116/) (2021)
- [DRo: A data-scarce mechanism to revolutionize the performance of Deep Learning based Security Systems](https://mlsanity.com/paper/2109.05470/) (2021)
- [Towards Trustworthy Keylogger detection: A Comprehensive Analysis of Ensemble Techniques and Feature Selections through Explainable AI](https://mlsanity.com/paper/2505.16103/) (2025)
- [Beyond the Wrapper: Identifying Artifact Reliance in Static Malware Classifiers using TRUSTEE](https://mlsanity.com/paper/2605.07034/) (2026)

## Abstract (arXiv)

Research shows that over the last decade, malware has been growing exponentially, causing substantial financial losses to various organizations. Different anti-malware companies have been proposing solutions to defend attacks from these malware. The velocity, volume, and the complexity of malware are posing new challenges to the anti-malware community. Current state-of-the-art research shows that recently, researchers and anti-virus organizations started applying machine learning and deep learning methods for malware analysis and detection. We have used opcode frequency as a feature vector and applied unsupervised learning in addition to supervised learning for malware classification. The focus of this tutorial is to present our work on detecting malware with 1) various machine learning algorithms and 2) deep learning models. Our results show that the Random Forest outperforms Deep Neural Network with opcode frequency as a feature. Also in feature reduction, Deep Auto-Encoders are overkill for the dataset, and elementary function like Variance Threshold perform better than others. In addition to the proposed methodologies, we will also discuss the additional issues and the unique challenges in the domain, open research problems, limitations, and future directions.