TY - GEN
T1 - Comparative Analysis of Android Malware Classification Feature Selection on SVM Using PSO
AU - Ghufron, Muhammad Zakky
AU - Putra, Muhammad Aidiel Rachman
AU - Ahmad, Tohari
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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%
AB - 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%
KW - android malware
KW - information security
KW - network infrastructure
KW - particle swarm optimization
KW - support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85210524074&partnerID=8YFLogxK
U2 - 10.1109/ICITISEE63424.2024.10730553
DO - 10.1109/ICITISEE63424.2024.10730553
M3 - Conference contribution
AN - SCOPUS:85210524074
T3 - 2024 8th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2024
SP - 82
EP - 86
BT - 2024 8th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2024
Y2 - 29 August 2024 through 30 August 2024
ER -