Algoritma LightGBM untuk Deteksi Aktivitas Cyber Espionage Melalui Dataset Serangan Siber
DOI:
https://doi.org/10.70609/jusifor.v4i2.8489Kata Kunci:
Espionage Siber, LightGBM, Algoritma Machine Learning,, Deteksi DiniAbstrak
Spionase siber adalah jenis serangan siber di mana peretas mencoba mencuri informasi penting secara rahasia dan terus-menerus melalui jaringan komputer. Studi ini mengusulkan penggunaan algoritma Light Gradient Boosting Machine (LightGBM) untuk mendeteksi tanda-tanda awal spionase digital. Data yang digunakan berasal dari dataset Cybersecurity Intrusion Detection yang tersedia di Kaggle. Penelitian ini mencakup langkah-langkah seperti membersihkan dan mengorganisir data, membaginya menjadi 80% untuk pelatihan dan 20% untuk pengujian, serta melatih model dengan pengaturan yang dipilih secara hati-hati untuk laju pembelajaran dan jumlah daun. Hasil menunjukkan bahwa model LightGBM berkinerja baik, dengan akurasi 89%, AUC 0.874, dan presisi rata-rata 0.9064. Untuk kelas serangan, model memiliki presisi 1,00 dan recall 0,75. Fitur-fitur paling penting yang membantu mengidentifikasi perilaku mencurigakan adalah ip_reputation_score, session_duration, dan network_packet_size. Deteksi dini dilakukan dengan menganalisis pola-pola tidak biasa dalam data untuk menemukan aktivitas jaringan yang mirip dengan spionase siber. Dibandingkan dengan metode lain seperti SVM dan Random Forest, LightGBM bekerja lebih baik dan lebih cepat. Berdasarkan hasil ini, model LightGBM dianggap sebagai alat yang baik untuk sistem peringatan dini dalam mendeteksi spionase siber.
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