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All Journal ELKHA : Jurnal Teknik Elektro Semantik Proceeding SENDI_U JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Building of Informatics, Technology and Science Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Informatika dan Rekayasa Perangkat Lunak Abdimasku : Jurnal Pengabdian Masyarakat Journal of Intelligent Computing and Health Informatics (JICHI) Jurnal Teknik Informatika (JUTIF) Jurnal Informatika: Jurnal Pengembangan IT INOVTEK Polbeng - Seri Informatika
Fauzi Adi Rafrastara
Fakultas Ilmu Komputer, Universitas Dian Nuswantoro, Semarang 50131
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Other

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Journal : Jurnal Sisfokom (Sistem Informasi dan Komputer)

 1 
Enhancing XGBoost Performance in Malware Detection through Chi-Squared Feature Selection Rosyada, Salma; Rafrastara, Fauzi Adi; Ramadhani, Arsabilla; Ghozi, Wildanil; Yassin, Warusia
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2293

Abstract

The increasing prevalence of malware poses significant risks, including data loss and unauthorized access. These threats manifest in various forms, such as viruses, Trojans, worms, and ransomware. Each continually evolves to exploit system vulnerabilities. Ransomware has seen a particularly rapid increase, as evidenced by the devastating WannaCry attack of 2017 which crippled critical infrastructure and caused immense economic damage. Due to their heavy reliance on signature-based techniques, traditional anti-malware solutions struggle to keep pace with malware's evolving nature. However, these techniques face limitations, as even slight code modifications can allow malware to evade detection. Consequently, this highlights weaknesses in current cybersecurity defenses and underscores the need for more sophisticated detection methods. To address these challenges, this study proposes an enhanced malware detection approach utilizing Extreme Gradient Boosting (XGBoost) in conjunction with Chi-Squared Feature Selection. The research applied XGBoost to a malware dataset and implemented preprocessing steps such as class balancing and feature scaling. Furthermore, the incorporation of Chi-Squared Feature Selection improved the model's accuracy from 99.1% to 99.2% and reduced testing time by 89.28%, demonstrating its efficacy and efficiency. These results confirm that prioritizing relevant features enhances both the accuracy and computational speed of the model. Ultimately, combining feature selection with machine learning techniques proves effective in addressing modern malware detection challenges, not only enhancing accuracy but also expediting processing times.             
Comparative Analysis of Feature Selection Methods with XGBoost for Malware Detection on the Drebin Dataset Latifah, Ines Aulia; Rafrastara, Fauzi Adi; Bintoro, Jevan; Ghozi, Wildanil; Osman, Waleed Mahgoub
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2294

Abstract

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 detection methods are increasingly needed to counter these evolving threats. This study aims to compare the performance of various feature selection methods combined with the XGBoost algorithm for malware detection using the Drebin dataset, and to identify the best feature selection method to enhance accuracy and efficiency. The experimental results show that XGBoost with the Information Gain method achieves the highest accuracy of 98.7%, with faster training times than other methods like Chi-Squared and ANOVA, which each achieved an accuracy of 98.3%. Information Gain yielded the best performance in accuracy and training time efficiency, while Chi-Squared and ANOVA offered competitive but slightly lower results. This study highlights that appropriate feature selection within machine learning algorithms can significantly improve malware detection accuracy, potentially aiding in real-world cybersecurity applications to prevent harmful cyberattacks.
Enhancing XGBoost Performance in Malware Detection through Chi-Squared Feature Selection Rosyada, Salma; Rafrastara, Fauzi Adi; Ramadhani, Arsabilla; Ghozi, Wildanil; Yassin, Warusia
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2293

Abstract

The increasing prevalence of malware poses significant risks, including data loss and unauthorized access. These threats manifest in various forms, such as viruses, Trojans, worms, and ransomware. Each continually evolves to exploit system vulnerabilities. Ransomware has seen a particularly rapid increase, as evidenced by the devastating WannaCry attack of 2017 which crippled critical infrastructure and caused immense economic damage. Due to their heavy reliance on signature-based techniques, traditional anti-malware solutions struggle to keep pace with malware's evolving nature. However, these techniques face limitations, as even slight code modifications can allow malware to evade detection. Consequently, this highlights weaknesses in current cybersecurity defenses and underscores the need for more sophisticated detection methods. To address these challenges, this study proposes an enhanced malware detection approach utilizing Extreme Gradient Boosting (XGBoost) in conjunction with Chi-Squared Feature Selection. The research applied XGBoost to a malware dataset and implemented preprocessing steps such as class balancing and feature scaling. Furthermore, the incorporation of Chi-Squared Feature Selection improved the model's accuracy from 99.1% to 99.2% and reduced testing time by 89.28%, demonstrating its efficacy and efficiency. These results confirm that prioritizing relevant features enhances both the accuracy and computational speed of the model. Ultimately, combining feature selection with machine learning techniques proves effective in addressing modern malware detection challenges, not only enhancing accuracy but also expediting processing times.             
Comparative Analysis of Feature Selection Methods with XGBoost for Malware Detection on the Drebin Dataset Latifah, Ines Aulia; Rafrastara, Fauzi Adi; Bintoro, Jevan; Ghozi, Wildanil; Osman, Waleed Mahgoub
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2294

Abstract

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 detection methods are increasingly needed to counter these evolving threats. This study aims to compare the performance of various feature selection methods combined with the XGBoost algorithm for malware detection using the Drebin dataset, and to identify the best feature selection method to enhance accuracy and efficiency. The experimental results show that XGBoost with the Information Gain method achieves the highest accuracy of 98.7%, with faster training times than other methods like Chi-Squared and ANOVA, which each achieved an accuracy of 98.3%. Information Gain yielded the best performance in accuracy and training time efficiency, while Chi-Squared and ANOVA offered competitive but slightly lower results. This study highlights that appropriate feature selection within machine learning algorithms can significantly improve malware detection accuracy, potentially aiding in real-world cybersecurity applications to prevent harmful cyberattacks.
Co-Authors Abdollah, Mohd. Faizal Abdussalam Abdussalam, Abdussalam Adhitya Nugraha Adhitya Nugraha Ahmed, Foez Al Fahreza, Muhammad Daffa Amalia, Syafira Rosa Amiral, Afinzaki Anargya, Muhammad Alden Nayef Astuti, Yani Parti Bintoro, Jevan Catur Supriyanto Catur Supriyanto Catur Supriyanto Cinantya Paramita Cinantya Paramita Dewi Pergiwati Dwi Eko Waluyo Dwi Puji Prabowo Dwi Puji Prabowo, Dwi Puji Egia Rosi Subhiyakto, Egia Rosi Erlin Dolphina Etika Kartikadarma Firmansyah, Muhammad David Foez Ahmed Ghozi, Wildani Hayu Wikan Kinasih Heru Lestiawan Hussein, Jasim Nadheer Hussein, Jassim Nadheer Ifan Rizqa Ilma, Zidni Kencana, Lisdi Inu L. Budi Handoko Latifah, Ines Aulia Lisdi Inu Kencana Mahendra, Syafrie Naufal Megantara, Rama Aria Nohan, Rajendra Osman, Waleed Mahgoub Paramita, Cinantya Pergiwati, Dewi Pulung Nurtantio Andono Putra Hartanto, Octaviano Ryan Eka Ramadhan Rakhmat Sani Ramadhani, Arsabilla Ricardus Anggi Pramunendar Rosyada, Salma Saputra, Ardi Sudibyo, Usman T. Sutojo Usman Sudibyo Widyatama, Thierry Wildanil Ghozi Yani Parti Astuti Yani Parti Astuti Yassin, Warusia