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

TL;DR

This paper introduces a High Performance-ELM method for malware detection, demonstrating superior accuracy on two datasets, enhancing cybersecurity anomaly detection with a novel machine learning approach.

Contribution

The paper presents a novel HP-ELM method tailored for malware detection, showing improved accuracy over existing techniques on standard datasets.

Findings

HP-ELM achieved 95.92% accuracy on malware datasets

The method outperforms traditional ML approaches in anomaly detection

Effective with only top 3 features and one activation function

Abstract

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.