# Detection of Advanced Malware by Machine Learning Techniques

arXiv: https://arxiv.org/abs/1903.02966 · HTML: https://mlsanity.com/paper/1903.02966/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2019.

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

- [Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks](https://mlsanity.com/paper/2107.05172/) (2021)
- [Malware Detection and Prevention using Artificial Intelligence Techniques](https://mlsanity.com/paper/2206.12770/) (2022)
- [Artificial Intelligence for Cybersecurity: Threats, Attacks and Mitigation](https://mlsanity.com/paper/2209.13454/) (2022)
- [xDNN(ASP): Explanation Generation System for Deep Neural Networks powered by Answer Set Programming](https://mlsanity.com/paper/2601.03847/) (2026)
- [Beyond the Wrapper: Identifying Artifact Reliance in Static Malware Classifiers using TRUSTEE](https://mlsanity.com/paper/2605.07034/) (2026)

## Abstract (arXiv)

In today's digital world most of the anti-malware tools are signature based which is ineffective to detect advanced unknown malware viz. metamorphic malware. In this paper, we study the frequency of opcode occurrence to detect unknown malware by using machine learning technique. For the purpose, we have used kaggle Microsoft malware classification challenge dataset. The top 20 features obtained from fisher score, information gain, gain ratio, chi-square and symmetric uncertainty feature selection methods are compared. We also studied multiple classifier available in WEKA GUI based machine learning tool and found that five of them (Random Forest, LMT, NBT, J48 Graft and REPTree) detect malware with almost 100% accuracy.