Systematic Literature Review: Evolusi Ancaman Siber Dan Metode Deteksi Malware Di Sistem Operasi Android (2020–2025)
Penulis
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Nuansa Bening Aura Jelita
Universitas Pendidikan Indonesia
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Herbert Siregar
Universitas Pendidikan Indonesia
DOI:
https://doi.org/10.62712/juktisi.v4i1.395Kata Kunci:
Mobile Operating System, Malware Detection, Android Malware, Systematic Literature Review, PRISMAAbstrak
Android sebagai sistem operasi mobile dominan menghadapi ancaman siber kompleks seperti logic bombs, repackaging attack, banking trojans, dan botnet. Metode deteksi berbasis Machine Learning (ML) dan Deep Learning (DL), seperti Covalent Bond Strength Score (akurasi 97,5%) dan Zero Trust Architecture (ZTA) untuk deteksi proaktif TTP, menunjukkan hasil yang menjanjikan. Analisis graph-based seperti Triadic Suspicion Graph (TSG) mencapai akurasi 99,9% dalam mendeteksi banking trojans, sementara metode hybrid berbasis NLP dan virtualisasi ARM-based membantu mengatasi eksploitasi runtime dan teknik penghindaran. Tantangan utama meliputi keterbatasan dataset malware dan serangan adversarial terhadap model AI. Studi ini menggunakan Systematic Literature Review (SLR) dengan pedoman PRISMA untuk memberikan gambaran komprehensif perkembangan deteksi malware Android. Temuan diharapkan mendukung pengembangan sistem keamanan yang lebih efektif dan adaptif.
Unduhan
Referensi
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Hak Cipta (c) 2025 Nuansa Bening Aura Jelita, Herbert Siregar
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