Comparative Analysis of Android Malware Classification Feature Selection on SVM Using PSO

Muhammad Zakky Ghufron, Muhammad Aidiel Rachman Putra, Tohari Ahmad

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Android operating system provides numerous applications within the Google Play service that can facilitate human activities. However, the lack of infrastructure security of some applications might cause malware infections. This situation potentially threatens the Android system and user data on mobile devices. To carry out this issue, the use of machine learning for malware classification has been popular in recent studies. However, as the preprocessing step, feature selection method was very important which can affect performance on machine learning. Therefore, this study proposed a comparative analysis using Particle Swarm Optimization as feature selection on the Support Vector Machine model for classifying Android malware. This experiment used CICMalDroid2020 dataset to compare Particle Swarm Optimization and Gain Ratio feature as a state-of-the-art method. The result showed that Particle Swarm Optimization was underperformed compared to the Gain Ratio method, achieved a 77.80% accuracy, 78.30% precision, 77.80% recall, and 77.35%

Original languageEnglish
Title of host publication2024 8th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages82-86
Number of pages5
ISBN (Electronic)9798350368970
DOIs
Publication statusPublished - 2024
Event8th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2024 - Hybrid, Yogyakarta, Indonesia
Duration: 29 Aug 202430 Aug 2024

Publication series

Name2024 8th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2024

Conference

Conference8th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2024
Country/TerritoryIndonesia
CityHybrid, Yogyakarta
Period29/08/2430/08/24

Keywords

  • android malware
  • information security
  • network infrastructure
  • particle swarm optimization
  • support vector machine

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