Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Journal Technology Information and Data Analytic
Prasetyo, Arya Adhari
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Engineering Library & Information Science Other

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Deteksi Serangan Brute Force SSH Menggunakan Klasifikasi Naïve Bayes pada Log Cowrie Honeypot di Lingkungan Virtual Prasetyo, Arya Adhari; Herianto; Yahya; Syamsiyah, Nur
Journal TIFDA (Technology Information and Data Analytic) Vol 2 No 1 (2025): Journal Technology Information and Data Analytic (TIFDA)
Publisher : Prodi Teknologi Informasi Universitas Darma Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70491/tifda.v2i1.88

Abstract

The increasing number of brute force cyberattacks targeting SSH services highlights the urgent need for effective early detection and mitigation systems. This study aims to analyze brute force attack patterns using the Naïve Bayes classification algorithm based on log data generated by the Cowrie Honeypot. A simulated virtual environment was developed to emulate attack scenarios and generate authentic SSH log data while preserving real server confidentiality. The system architecture follows the CRISP-DM framework, including data preprocessing, model development, evaluation, and deployment. Evaluation using confusion matrix metrics showed that the Naïve Bayes algorithm successfully distinguished brute force attempts from normal traffic with high accuracy, precision, recall, and F1-score. The findings confirm the potential of combining Cowrie honeypot data with machine learning classifiers as an early warning tool for intrusion detection in enterprise network infrastructures.
Co-Authors Herianto Nur Syamsiyah Yahya