Pemanfaatan Machine Learning dalam Keamanan Siber: Tinjauan Literatur Sistematis tentang Lanskap Terkini dan Tren Masa Depan

Wahyudi

Penulis

Wahyudi Universitas Horizon Indonesia

Kata Kunci:

Machine Learning; Deteksi Serangan Siber; Deep Learning; Intrusion Detection; Keamanan Jaringan; Systematic Literature Review

Abstrak

Ancaman siber terus berkembang dalam skala dan kecanggihan dengan kecepatan yang tidak lagi mampu diimbangi oleh perlindungan berbasis aturan. Artikel ini melaporkan sebuah Tinjauan Literatur Sistematis ( Systematic Literature Review /SLR) yang menggali enam data dasar akademik dan memilih 19 publikasi , sembilan studi primer (2020–2025) dan sepuluh studi fondasi (sebelum 2020). Model deep learning , khususnya CNN dan LSTM, mencapai akurasi deteksi di atas 95%, namun membutuhkan sumber daya komputasi yang besar; Pendekatan klasik seperti Random Forest dan SVM tetap praktis digunakan ketika kecepatan inferensi dan kemampuan interpretasi lebih diprioritaskan daripada akurasi puncak. Dari keseluruhan kajian yang diulas, terdapat empat tantangan implementasi yang paling sering muncul: keruntuhan kelas dalam data pelatihan, kendala latensi waktu nyata, kerentanan terhadap masukan adversarial, serta kurangnya transparansi pada model deep learning. Selanjutnya, pembelajaran gabungan, AI yang dapat dijelaskan (XAI), dan integrasi dengan platform Security Orchestration , Automation and Response (SOAR) menjadi topik yang paling aktif mendapat perhatian dalam investasi penelitian. Kontribusi pembeda dari observasi ini adalah klarifikasi yang eksplisit antara bukti empiris terkini (2020–2025) dan karya fondasi sebelum tahun 2020 dalam satu kerangka SLR terpadu, sehingga memungkinkan atribusi temuan yang lebih jelas antara perkembangan kontemporer dan historis

Referensi

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