# A New Malware Detection System Using a High Performance-ELM method

arXiv: https://arxiv.org/abs/1906.12198 · HTML: https://mlsanity.com/paper/1906.12198/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2019.

## Built on by (most cited first, 2 of 2)

- [CNN vs ELM for Image-Based Malware Classification](https://mlsanity.com/paper/2103.13820/) (2021)
- [ENCODE: Encoding NetFlows for Network Anomaly Detection](https://mlsanity.com/paper/2207.03890/) (2022)

## Abstract (arXiv)

A vital element of a cyberspace infrastructure is cybersecurity. Many protocols proposed for security issues, which leads to anomalies that affect the related infrastructure of cyberspace. Machine learning (ML) methods used to mitigate anomalies behavior in mobile devices. This paper aims to apply a High Performance Extreme Learning Machine (HP-ELM) to detect possible anomalies in two malware datasets. Two widely used datasets (the CTU-13 and Malware) are used to test the effectiveness of HP-ELM. Extensive comparisons are carried out in order to validate the effectiveness of the HP-ELM learning method. The experiment results demonstrate that the HP-ELM was the highest accuracy of performance of 0.9592 for the top 3 features with one activation function.