Literature Review: Taksonomi Artificial Intelligence pada Mekanisme Kriptografi
Abstract
Meningkatnya ancaman siber dan komputasi kuantum menimbulkan ancaman signifikan terhadap integritas, kerahasiaan, dan ketersediaan data sensitif. Meskipun berbagai skema keamanan terus diajukan, mekanisme kriptografi konvensional mulai menghadapi titik jenuh dalam hal efisiensi, di samping itu pengamanan sirkuit dari kebocoran data di lingkungan terdistribusi masih menjadi tantangan besar. Tujuan utama dari penelitian ini adalah untuk mengetahui perkembangan implementasi kecerdasan artifisial (AI) pada mekanisme kriptografi agar parameter keamanan data dapat dioptimalkan secara dinamis. Systematic Literature Review dilakukan untuk mengklasifikasikan studi-studi penting guna mencapai tujuan tersebut, dari 86 publikasi penelitian yang diambil dari sumber IEEE Xplore, Google Scholar, ScienceDirect, dan lainnya, berdasarkan kriteria inklusi, eksklusi, dan penilaian kualitas, sebanyak 40 penelitian final dipilih dan dianalisis secara mendalam. Penelitian ini mensintesis taksonomi yang memetakan berbagai teknik AI seperti Machine Learning, Deep Learning, dan Adversarial Networks, serta mengevaluasi 22 risiko data spesifik dan strategi mitigasinya di setiap domain kriptografi modern seperti Homomorphic Encryption dan Post-Quantum Cryptography, serta peluang riset di masa depan dalam domain implementasi AI pada mekanisme kriptografi. Hasilnya, 38,46% menyatakan bahwa penerapan AI dalam teknik kriptografi diimplementasikan dalam lingkungan cloud. Selain itu, 37,5% menyatakan tantangan keamanan dalam integrasi AI adalah beban komputasi, penumpukan noise FHE dan ambiguitas black-box AI itu sendiri. Temuan dari penelitian ini membantu para praktisi dan pengembang sistem keamanan siber dalam meningkatkan ketahanan kerangka keamanan data, serta mengoptimalkan efisiensi komputasi enkripsi secara adaptif
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