Implementasi Teknik Seleksi Fitur Pada Klasifikasi Malware Android Menggunakan Support Vector Machine

Hendra Saputra, Setio Basuki, Mahar Faiqurahman


Android Malware has grown significantly along with the advance of the times and the increasing variety of technique in the development of Android. Machine Learning technique is a method that now we can use in the modeling the pattern of a static and dynamic feature of Android Malware. In the level of accuracy of the Malware type classification, the researcher connect between the application feature with the feature required by each type of Malware category. The category of malware used is a type of Malware that many circulating today, to classify the type of Malware in this study used Support Vector Machine (SVM). The SVM type will be used is class SVM one against one using the RBF Kernel. The feature will be used in this classification are the Permission and Broadcast Receiver.  To improve the accuracy of the classification result in this study used Feature Selection method. Selection of feature used is Correlation-based Feature Selection (CFS), Gain Ratio (GR) and Chi-Square (CHI). A result from Feature Selection will be evaluated together with result that not use Feature Selection. Accuracy Classification Feature Selection CFS result accuracy of 90.83%, GR and CHI of 91.25% and data that not use Feature Selection of 91.67%. The result of testing indicates that permission and broadcast receiver can be used in classifying type of Malware, but the Feature Selection method that used have accuracy is a little below the data that are not using Feature Selection.


Classification Android Malware, Feature Selection, SVM and Multi Class SVM one against one

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N. Yadav, A. Sharma, and A. Doegar, “A Survey on Android Malware Detection,” vol. 7, no. 12, pp. 47–53, 2016., “Number of Android applications,” 2017. [Online]. Available: . [Accessed: 25-Jan-2017].

J. Rivera and R. van der Meulen, “Gartner Says Annual Smartphone Sales Surpassed Sales of Feature Phones for the First Time in 2013,” Gartner Newsroom, 2014. [Online]. Available:

C. La and P. Myo, “Manifest Files Classification of,” vol. 2, no. 2, pp. 119–133, 2014.

V. Wahanggara and Y. Prayudi, “Sistem Deteksi Malicious Software Berbasis System Call untuk Klasifikasi Barang Bukti Digital Menggunakan Metode Support Vector Machine,” SENTRA (Seminar Nas. Teknol. dan Rekayasa), no. July, pp. 1–8, 2015.

S. P. Chorghe and N. Shekokar, “An Innovative Technique to Detect Malicious Applications in Android,” Int. J. Sci. Res., vol. 4, no. 12, pp. 2013–2016, 2015.

N. Peiravian and X. Zhu, “Machine learning for Android malware detection using permission and API calls,” in Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI, 2013, pp. 300–305.

S. Y. Yerima, S. Sezer, and G. McWilliams, “Analysis of Bayesian Classification-based Approaches for Android Malware Detection,” Inf. Secur. IET, vol. 8, no. July 2013, pp. 25–36, 2014.

S. Yerima and G. Mcwilliams, “Machine Learning based malware detection for Android using static analysis,” no. January, 2012.

E. Rasywir and A. Purwarianti, “Eksperimen pada Sistem Klasifikasi Berita Hoax Berbahasa Indonesia Berbasis Pembelajaran Mesin,” J. Cybermatika, vol. 3, no. 2, pp. 1–8, 2015.

T. Djatna and Y. Morimoto, “Pembandingan Stabilitas Algoritma Seleksi Fitur menggunakan Transformasi Ranking Normal,” J. Ilmu Komput., vol. 6, no. 2, pp. 1–6, 2008.

H. Yu, X. Huang, X. Hu, and H. Cai, “A Comparative Study on Data Mining Algorithms for Individual Credit Risk Evaluation,” in 2010 International Conference on Management of e-Commerce and e-Government, 2010, pp. 35–38.

D. Zhang, H. Huang, Q. Chen, and Y. Jiang, “A comparison study of credit scoring models,” in Proceedings - Third International Conference on Natural Computation, ICNC 2007, 2007, vol. 1, pp. 15–18.


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