Comparative Analysis of Feature Selection Methods with XGBoost for Malware Detection on the Drebin Dataset
Malware, or malicious software, continues to evolve alongside increasing cyberattacks targeting individual devices and critical infrastructure. Traditional detection methods, such as signature-based detection, are often ineffective against new or polymorphic malware. Therefore, advanced malware dete...
Saved in:
| Main Authors: | Ines Aulia Latifah, Fauzi Adi Rafrastara, Jevan Bintoro, Wildanil Ghozi, Waleed Mahgoub Osman |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
LPPM ISB Atma Luhur
2024-11-01
|
| Series: | Jurnal Sisfokom |
| Subjects: | |
| Online Access: | https://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/2294 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Integrating Information Gain and Chi-Square for Enhanced Malware Detection Performance
by: Fauzi Adi Rafrastara, et al.
Published: (2025-01-01) -
A Survey on Android Malware Detection Techniques Using Supervised Machine Learning
by: Safa J. Altaha, et al.
Published: (2024-01-01) -
Is Malware Detection Needed for Android TV?
by: Gokhan Ozogur, et al.
Published: (2025-03-01) -
Android Malware Category and Family Identification Using Parallel Machine Learning
by: Ahmed Hashem El Fiky, et al.
Published: (2022-07-01) -
Enhancing the Sustainability of Machine Learning-Based Malware Detection Techniques for Android Applications
by: Seyeon Park, et al.
Published: (2025-01-01)