Deteksi Malware Android Menggunakan Pengklasifikasi Pembelajaran Mesin Paralel
Abstract
Abstrak — Perkembangan android semakin pesat, sehingga mendorong pertumbuhan malware android. Data malware android memiliki dimensi tinggi, dibutuhkan algoritme untuk melakukan deteksi. Support Vector Machine (SVM) adalah algoritme pembelajaran mesin yang cocok untuk data malware android. Namun SVM memiliki keterbatasan dari segi waktu komputasi untuk data dalam jumlah besar, karena membutuhkan solusi dari masalah pengoptimalan Quadratic Programming (QP). Penelitian ini mengusulkan Parallel Support Vector Machine (PSVM) dengan metode dekomposisi Sequential Minimal Optimization (SMO) untuk melakukan deteksi atau klasifikasi malware android menggunakan dataset DREBIN. algoritme SMO yang dijamin memecahkan QP, dengan menggunakan teknik dekomposisi yaitu mendistribusikan tugas ke beberapa prosesor untuk dieksekusi secara paralel. Evaluasi berdasarkan kinerja perbandingan model Parallel SVM-SMO 4 dekomposisi dan Non-Parallel SVM-SMO dengan analisis fitur menggunakan Correlation Coefficient. Pada pengujian metrik performa dan akurasi fitur paling optimal 14 fitur dengan rata-rata akurasi 78%. Pada pengujian kinerja model, fitur paling optimal 27 fitur dengan rata-rata percepatan 9.58 dan efisiensi 2.39 pada kernel linier.
Kata Kunci— DREBIN, SVM, PSVM, SMO, Dekomposisi, Koefisien Korelasi
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