Deteksi URL Phishing menggunakan Model Long Short Term Memory

• RAHMAT SATRIA BUANA
• 14210231

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ABSTRAK

ABSTRAK

Nama              : Rahmat Satria Buana

NIM                 : 14210231

Program Studi : Ilmu Komputer

Fakultas          : Teknologi Informasi

Jenjang           : Strata Dua (S2)

Konsentrasi     : Data Mining

Judul              : Deteksi URL Phishing menggunakan Model Long Short Term Memory

Pertumbuhan global yang pesat telah mengakibatkan meningkatnya jumlah pengguna internet dan aplikasi online sehingga transaksi data pribadi melalui internet juga semakin naik volumenya. Hal ini memudahkan penyerang untuk mendapatkan akses data dan layanan penting untuk melakukan berbagai aktivitas kejahatan siber, salah satunya phising. Penyerang menjebak pengguna ke situs phising untuk mencuri kredensial pengguna dan data pribadi lainnya yang diperlukan untuk menyamar sebagai korban mereka. Alat phising canggih dan jaringan fluks semakin banyak digunakan oleh penyerang untuk membuat dan menghosting situs phising, dengan tujuan untuk meningkatkan jumlah serangan phising dan menghindari deteksi. Hal ini mengakibatkan peningkatan jumlah situs phising baru (zero-day). Perangkat lunak anti-malware dan filter anti-phising browser web banyak digunakan untuk mendeteksi situs phising sehingga pengguna terhindar dari phising. Namun demikian solusi ini sebagian besar bergantung pada daftar hitam situs phising yang dikenal. Jeda waktu antara pembuatan situs phising baru dan pelaporannya tidak seimbang sehingga pengguna dapat terpapar ke situs phising. Hal ini berkontribusi pada peningkatan jumlah serangan phising yang berhasil dalam beberapa tahun terakhir. Pada penelitian ini dilakukan analisis deteksi web phising dengan menggunakan model Long Short Term Memory, untuk mendapatkan tingkat akurasi terbaik.

KATA KUNCI

Big Data,machine learning,Deep Learning,Data Mining,LSTM

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DAFTAR PUSTAKA

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