Peningkatan Peforma Deteksi Serangan menggunakan Metode PCA dan Forest

Penulis

  • Eko Arip Winanto Universitas Dinamika Bangsa, Surabaya
  • Yudi Novianto Universitas Dinamika Bangsa, Surabaya
  • Sharipuddin Sharipuddin Universitas Dinamika Bangsa, Surabaya
  • Ibnu Sani Wijaya Universitas Dinamika Bangsa, Surabaya
  • Pareza Alam Jusia Universitas Dinamika Bangsa, Surabaya

DOI:

https://doi.org/10.25126/jtiik.20241127678

Abstrak

Keamanan jaringan menjadi hal yang sangat penting dalam menghadapi ancaman serangan yang semakin kompleks dan canggih. Deteksi serangan dalam jaringan dapat membantu mengidentifikasi aktivitas mencurigakan yang mengindikasikan upaya penetrasi atau serangan oleh pihak yang tidak berwenang. Dalam upaya untuk meningkatkan performa deteksi serangan pada jaringan IoT perlu adanaya penerapan sebuah metode untuk mendeteksi sebuah ancaman . Metode Random Forest adalah algoritma pembelajaran mesin yang memanfaatkan ansambel pohon keputusan. Ansambel tersebut terdiri dari beberapa pohon keputusan independen yang digunakan untuk mengklasifikasikan data. Salah satu karakteristik dari metode Random Forest adalah kemampuannya dalam mengatasi masalah overfitting dan kualitas prediksi yang baik. Principal Component Analysis (PCA) adalah teknik statistik yang digunakan untuk mengurangi dimensi data dengan memproyeksikannya ke ruang fitur yang lebih rendah. Hal ini membantu menghilangkan korelasi antar fitur dan mengidentifikasi fitur-fitur penting yang dapat meningkatkan pemisahan antara serangan dan lalu lintas normal. Dalam penelitian ini akan diujikan dengan dataset CIC IOT 2023 yang terdiri dari beberapa tipe serangan yaitu DDoS, DoS, Recon, Web-based, Brute Force, Spoofing, dan Mirai. Pengujian model  terdiri dari 4 fitur  yaitu 5,8,10 dan 47. Hasil deteksi menunjukkan hasil yang memuaskan dengan meningkatkan kinerja dalam mendeteksi serangan hingga mencapai 99,2%

 

Abstract

Network security has become increasingly critical in the face of complex and sophisticated threat attacks. Detecting intrusions within a network can aid in identifying suspicious activities indicative of unauthorized penetration attempts or attacks. To enhance intrusion detection performance, the implementation of a method for threat detection is necessary. The Random Forest method, an ensemble machine learning algorithm that leverages multiple independent decision trees, is employed in this study. This method effectively addresses overfitting issues and demonstrates good predictive quality. Principal Component Analysis (PCA), a statistical technique for dimensionality reduction, is utilized to project data into a lower-dimensional feature space. By eliminating correlations between features and identifying important ones, PCA enhances the separation between attacks and normal traffic. This research utilizes the CIC IOT 2023 dataset, encompassing various types of attacks such as DDoS, DoS, Recon, Web-based, Brute Force, Spoofing, dan Mirai. The model testing phase incorporates 4 features: 5, 8, 10, and 47. The detection results indicate a remarkable performance improvement in identifying attacks, achieving an accuracy rate of 99.2%.

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Referensi

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Unduhan

Diterbitkan

25-04-2024

Terbitan

Bagian

Ilmu Komputer

Cara Mengutip

Peningkatan Peforma Deteksi Serangan menggunakan Metode PCA dan Forest. (2024). Jurnal Teknologi Informasi Dan Ilmu Komputer, 11(2), 285-290. https://doi.org/10.25126/jtiik.20241127678