Algoritma LightGBM untuk Deteksi Aktivitas Cyber Espionage Melalui Dataset Serangan Siber - Wulandari, Tasya; Yudisthira, Yasyfi Farhan; P.H, Kayla Chika; Wibowo, Muhammad Afriza; Andrew, Chistofer (JUSIFOR : Jurnal Sistem Informasi dan Informatika, 2025)
Cyber espionage is a type of cyberattack where hackers try to secretly and continuously steal important information through computer networks. This study suggests using the Light Gradient Boosting Machine (LightGBM) algorithm to spot early signs of digital espionage. The data used comes from the Cybersecurity Intrusion Detection dataset available on Kaggle. The research includes steps like cleaning and organizing the data, dividing it into 80% for training and 20% for testing, and training the model with carefully chosen settings for learning rate and number of leaves. The results show that the LightGBM model performed well, with an accuracy of 89%, an AUC of 0.874, and an average precision of 0.9064. For the attack class, the model had a precision of 1.00 and a recall of 0.75. The most important features that helped identify suspicious behavior were ip_reputation_score, session_duration, and network_packet_size. Early detection happens by looking at unusual patterns in the data to find network activities that look like cyber espionage. When compared to other methods like SVM and Random Forest, LightGBM works better and is faster. Based on these results, the LightGBM model is seen as a good tool for an early warning system to detect cyber espionage.