Sistem Deteksi Intrusi Berbasis Deep Learning untuk Mitigasi Serangan Zero-Day pada Jaringan Komputer
Kata Kunci:
Sistem Deteksi Penyusupan, Pembelajaran Mendalam, Serangan Zero-Day, CNN-LSTM, Keamanan Jaringan, Deteksi AnomaliAbstrak
Serangan siber zero-day semakin memperlihatkan risiko terhadap keamanan jaringan komputer karena tidak dapat dideteksi oleh sistem deteksi intrusi (IDS) konvensional yang hanya memanfaatkan pola tanda tangan. Penelitian ini bertujuan untuk merancang suatu sistem deteksi intrusi yang menggunakan deep learning, yang bisa mengenali dan mengatasi serangan zero-day dengan tepat dan secara langsung. Metode yang diterapkan adalah pendekatan kuantitatif eksperimental menggunakan dataset NSL-KDD dan UNSW-NB15, melalui tahap pengolahan data, pemilihan fitur, dan normalisasi. Model dilatih dengan arsitektur hibrida CNN-LSTM dan diuji dengan mengukur akurasi, precision, recall, F1-score, serta ROC-AUC. Hasil penelitian menunjukkan bahwa sistem ini berhasil mencapai akurasi 98,72%, precision 98,41%, recall 97,95%, dan F1-score 98,18%, serta mampu mengurangi tingkat false positive menjadi 12% dibandingkan dengan IDS yang berbasis signature. Sistem ini juga memiliki waktu respons rata-rata 0,84 detik, sehingga cocok digunakan dalam jaringan real-time. Oleh karena itu, sistem deteksi intrusi berbasis deep learning ini dinilai berhasil dalam mendeteksi serangan zero-day dengan cara yang adaptif dan efisien. Namun, kebutuhan akan sumber daya komputasi yang besar menjadi sebuah tantangan yang perlu diatasi melalui pengembangan lebih lanjut, seperti integrasi edge computing atau federated learning agar sistem menjadi lebih ringan dan mudah dikembangkan.
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Hak Cipta (c) 2025 Ahmad Farizi, Mohammad Khoirun Nizam (Penulis)
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