Open AccessAndroid APK Identification using Non Neural Network and Neural Network Classifier
Author(s) -
Djarot Hindarto,
Handri Santoso
Publication year - 2021
Publication title -
j-cosine (journal of computer science and informatics engineering)
Language(s) - English
Resource type - Journals
ISSN - 2541-0806
DOI - 10.29303/jcosine.v5i2.420
Subject(s) - computer science , artificial intelligence , decision tree , naive bayes classifier , perceptron , artificial neural network , machine learning , support vector machine , android (operating system) , multilayer perceptron , data mining , classifier (uml) , pattern recognition (psychology) , operating system
Currently adoption of mobile phones and mobile applications based on Android operating system is increasing rapidly. Many companies and emerging startups are carrying out digital transformation by using mobile applications to provide disruptive digital services to replace existing old styled services. This transformation prompted the attackers to create malicious software (malware) using sophisticate methods to target victims of Android mobile phone users. The purpose of this study is to identify Android APK files by classifying them using Artificial Neural Network (ANN) and Non Neural Network (NNN). The ANN is Multi-Layer Perceptron Classifier (MLPC), while the NNN are KNN, SVM, Decision Tree, Logistic Regression and Naïve Bayes methods. The results show that the performance using NNN has decreasing accuracy when training using larger datasets. The use of the K-Nearest Neighbor algorithm with a dataset of 600 APKs achieves an accuracy of 91.2% and dataset of 14170 APKs achieves an accuracy of 88%. The using of the Support Vector Machine algorithm with the 600 APK dataset has an accuracy of 99.1% and the 14170 APK dataset has an accuracy of 90.5%. The using of the Decision Tree algorithm with the 600 APK dataset has an accuracy of 99.2%, the 14170 APK dataset has an accuracy of 90.8%. The experiment using the Multi-Layer Perceptron Classifier has increasing with the 600 APK dataset reaching 99%, the 7000 APK dataset reaching 100% and the 14170 APK dataset reaching 100%.