Deteksi Serangan Brute Force pada Jaringan IoT Menggunakan Algoritma Machine Learning Berbasis Dataset CICIoT2023
Abstrak
Abstrak Perkembangan Internet of Things (IoT) yang dipakai banyak orang terus digunakan dengan berbagai perkembangan dan penemuan baru. Dalam proses perkembangannya dan waktu, seiringnya juga dengan munculnya tantangan yang dapat terjadi perangkat IoT dengan berbagai macam permasalahan. Salah satu kejadian dalam permasalahan IoT adalah serangan brute force yang merupakan upaya berbagai gabungan kredensial seperti kata sandi yang dilakukan untuk memaksa masuk ke akses perangkatnya hingga menemukan akses yang sah secara brutal atau banyak sekali percobaannya. Dari aktivitas tersebut dikumpulkan menjadi dataset CICIoT2023 yang dilakukan untuk mengenali aktivitas tersebut sebagai sistem deteksi intrusi (IDS) sehingga harapan kedepannya dapat memantau dan mencegah kerusakan yang tidak diinginkan dengan melakukan pembersihan data dan penerapan machine learning. Hasil perbandingan antara Random Forest dan Decision Tree menunjukkan bahwa akurasi mencapai 0.9466 dengan algoritma model Random Forest dengan teknik Random Undersampling (RUS) sehingga ini menunjukkan bahwa keefektivitas dari metode ensemble yang ada dalam meningkatkan stabilitas deteksi anomali serangan brute force pada lingkungan IoT. Kata kunci: Internet of Things, Machine Learning, Cyber Security, CICIoT2023, Intrusion Detection System
Unduhan
Referensi
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