Integrating Ensemble Learning and Information Gain for Malware Detection based on Static and Dynamic Features Article Swipe

MAKE IMPACT

Ramadhan Rakhmat Sani , Fauzi Adi Rafrastara , Wildanil Ghozi · YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.22219/kinetik.v10i1.2051

The rapid advancement of malware poses a significant threat to devices, like personal computers and mobile phones. One of the most serious threats commonly faced is malicious software, including viruses, worms, trojan horses, and ransomware. Conventional antivirus software is becoming ineffective against the ever-evolving nature of malware, which can now take on various forms like polymorphic, metamorphic, and oligomorphic variants. These advanced malware types can not only replicate and distribute themselves, but also create unique fingerprints for each offspring. To address this challenge, a new generation of antivirus software based on machine learning is needed. This intelligent approach can detect malware based on its behavior, rather than relying on outdated fingerprint-based methods. This study explored the integration of machine learning models for malware detection using various ensemble algorithms and feature selection techniques. The study compared three ensemble algorithms: Gradient Boosting, Random Forest, and AdaBoost. It used Information Gain for feature selection, analyzing 21 features. Additionally, the study employed a public dataset called ‘Malware Static and Dynamic Features VxHeaven and VirusTotal Data Set’, which encompasses both static and dynamic malware features. The results demonstrate that the Gradient Boosting algorithm combined with Information Gain feature selection achieved the highest performance, reaching an accuracy and F1-Score of 99.2%.

Type

article

Language

en

Landing Page

https://doi.org/10.22219/kinetik.v10i1.2051

OA Status

diamond

Related Works

10

OpenAlex ID

https://openalex.org/W4406707501

Raw OpenAlex JSON

OpenAlex ID

https://openalex.org/W4406707501

Canonical identifier for this work in OpenAlex

DOI

https://doi.org/10.22219/kinetik.v10i1.2051

Digital Object Identifier

Title

Integrating Ensemble Learning and Information Gain for Malware Detection based on Static and Dynamic Features

Work title

Type

article

OpenAlex work type

Language

en

Primary language

Publication year

2025

Year of publication

Publication date

2025-01-22

Full publication date if available

Authors

Ramadhan Rakhmat Sani, Fauzi Adi Rafrastara, Wildanil Ghozi

List of authors in order

Landing page

https://doi.org/10.22219/kinetik.v10i1.2051

Publisher landing page

Open access

Yes

Whether a free full text is available

OA status

diamond

Open access status per OpenAlex

OA URL

https://doi.org/10.22219/kinetik.v10i1.2051

Direct OA link when available

Concepts

Malware, Computer science, Information gain, Ensemble learning, Information gain ratio, Artificial intelligence, Machine learning, Computer security, Decision tree

Top concepts (fields/topics) attached by OpenAlex

Cited by

0

Total citation count in OpenAlex

Related works (count)

10

Other works algorithmically related by OpenAlex

Full payload