Klasifikasi dan Deteksi Malware Menggunakan Variasi Model Algoritma Machine learning
DOI:
https://doi.org/10.26418/jp.v11i1.87924Keywords:
Malware, SVM, KNN, Machine LearningAbstract
Serangan Malware merupakan serangan yang dilakukan oleh seorang attacker dengan cara mengirimkan kode-kode berbahaya ke berbagai file atau bahkan banyak paket dan server. Oleh karena itu, operasional jaringan yang handal menjadi faktor yang perlu diperhatikan untuk mencegah terjadinya serangan sedini mungkin agar tidak terjadi kerusakan sistem yang lebih parah. Jenis serangan dapat berupa Ping of Death, flooding, remote-controlled attack, UDP flooding, dan Smurf Attack. Data serangan diperoleh dari dataset ClaMP, selain itu dilakukan penangkapan paket data pada log jaringan dan optimasi ekstraksi fitur yang selanjutnya dianalisa secara statistik dengan algoritma machine learning. Tujuan dari penelitian ini adalah untuk mendeteksi, mengklasifikasi serangan Malware menggunakan berbagai model Algoritma ML seperti SVM, KNN dan Neural Network serta melakukan pengujian kinerja deteksi. Tahapan penelitian dimulai dari proses Pre-Processing, ekstraksi, pemilihan fitur dan klasifikasi serta pengujian kinerja. Data training dan testing pada penelitian ini menggunakan mixed model yaitu data division, split model dan cross validation. Hasil penelitian menyimpulkan bahwa algoritma terbaik untuk mendeteksi paket Malware adalah Neural Network untuk kategori Feature Combination dengan tingkat akurasi sebesar 96,91%, Recall sebesar 97,35% dan Precision sebesar 96,78%. Sehingga penelitian tersebut dapat berimplikasi bagi para ahli siber untuk dapat mencegah serangan Malware sejak dini. Sedangkan penelitian selanjutnya diperlukan algoritma khusus untuk meningkatkan deteksi serangan Malware, selain KNN, SVM dan Neural Network. Penelitian ini dapat dijadikan referensi bagi para peneliti yang sedang melakukan penelitian di bidang yang sama.References
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