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

Wahyudi

Authors

Wahyudi Universitas Horizon Indonesia

Keywords:

Intrusion Detection System, Adversarial Machine Learning, Federated Learning, Explainable AI, PRISMA

Abstract

Laju eskalasi ancaman siber yang melampaui kapasitas sistem deteksi berbasis aturan statis mendorong kebutuhan mendesak akan pendekatan deteksi yang adaptif dan berbasis data. Kajian ini bertujuan memetakan kinerja algoritma machine learning (ML) dalam mendeteksi serangan siber, mengidentifikasi tantangan implementasi yang berulang, serta mengevaluasi tingkat kematangan tren penelitian masa depan. Metode yang digunakan adalah Systematic Literature Review (SLR) mengacu pada panduan Carrera-Rivera et al. [6], dengan penelusuran sistematik pada enam basis data akademik: IEEE Xplore, ScienceDirect, SpringerLink, ACM Digital Library, Google Scholar, dan Portal Garuda. Dari 156 artikel yang teridentifikasi, 19 studi ditetapkan sebagai korpus final melalui prosedur PRISMA terdiri atas 9 studi primer (2020–2025) dan 10 studi landasan (sebelum 2020), setelah melewati penilaian kualitas dua dimensi: rigor metodologis dan relevansi industri. Hasil analisis menunjukkan bahwa CNN dan LSTM mencapai akurasi deteksi 97–99%, mengungguli Random Forest (95–98%) dan SVM (90–97%) dalam skenario klasifikasi kompleks. Empat tantangan implementasi yang paling konsisten dijumpai adalah ketidakseimbangan data latih, kendala latensi real-time, serangan adversarial, dan opasitas model deep learning. Dataset CICIDS2017 dan UNSW-NB15 semakin menggantikan posisi NSL-KDD sebagai standar evaluasi karena lebih merepresentasikan kondisi jaringan modern. Kajian ini menyimpulkan bahwa federated learning dan explainable AI (XAI) merupakan dua arah penelitian yang paling matang untuk mengatasi hambatan privasi data dan opasitas model. Kontribusi utama kajian ini terletak pada pemisahan eksplisit antara studi empiris terkini dan studi landasan historis dalam satu kerangka SLR terpadu, menghasilkan peta kinerja algoritma yang dapat langsung dijadikan acuan bagi praktisi keamanan dalam menentukan pendekatan ML yang sesuai dengan kapasitas infrastruktur organisasi.

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