Random Forest-Based DDOS Detection from Cpanel Logs with  Real-Time Notification Integration

Ridho Alfarizi; A M H Pardede; Husnul Khair

doi:10.59934/jaiea.v5i1.1502

Authors

Ridho Alfarizi STMIK Kaputama
A M H Pardede STMIK KAPUTAMA
Husnul Khair STMIK KAPUTAMA

DOI:

https://doi.org/10.59934/jaiea.v5i1.1502

Keywords:

Cybersecurity, DDoS Detection, Firebase Cloud Messaging, Machine Learning, Random Forest

Abstract

The study focuses on designing an automated program to detect Distributed Denial of Service (DDoS) attacks by analyzing access log data from CPanel. Using the Random Forest algorithm, the system processes large volumes of server log entries to distinguish between normal and malicious requests. Data preprocessing and model training are applied to optimize detection accuracy. To accelerate incident response, the detection module is integrated with Firebase Cloud Messaging (FCM), which delivers instant alerts to administrators when suspicious activity is identified. Experimental evaluation shows that the system achieves more than 95% accuracy on the test dataset, confirming its capability to reliably identify DDoS patterns. In comparison to manual analysis, the automated approach demonstrates superior speed, consistency, and operational efficiency, significantly reducing the time needed to recognize and respond to threats. The results indicate that combining machine learning-based detection with real-time notification is a practical and effective strategy for strengthening server security.

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