Classification Of Malware Families Using Naïve Bayes Classifier
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Abstract
Dikarenakan peningkatan pengguna smartphone Android berbanding lurus dengan peningkatan pengembangan malware yang semakin pesat. Tidak jarang penelitian tentang malware setiap tahunnya yang membahas tentang malware families dengan berbagai macam pendekatan yang salah satunya machine learning. Dengan mendapatkan data malware yang kredibel, dapat memudahkan peneliti dalam menganalisa malware. Terdapat kumpulan data malware yang dibuat the Canadian Institute for Cybersecurity (CIC) yang dapat diakses secara publik. Data ini disebut CICInvestAndMal2019 yang berisi data malware. Dataset ini dibuat dengan melakukan analisa statis dan dinamis pada smartphone secara real time. Hasil dari analisa tersebut kemudian diproses dengan metode Random Forest yang menghasilkan precision 61.2% dan recall 57.7%. Berdasarkan penelitian tersebut, maka penulis akan mengklasifikasikan dataset CICInvestAndMal2019 menggunakan metode Naïve Bayes, dan hasil yang didapat dari klasifikasi Naïve Bayes adalah nilai recall dan precision sebesar 68% dan 66%.
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