Year: 2025
Type: article
Abstract: The DDoS (Distributed Denial-of-Service) attack detection system aims to enhance network security across all aspects of internet technology utilization. One of its applications is in SPKLU (Public Electric Vehicle Charging Stations). This research aims to detect DDoS attacks using deep learning in network administration. The study employs a deep learning approach utilizing Convolutional Neural Network (CNN) on a public dataset. Based on our study and analysis results, CNN has a precision rate of 95%. The high accuracy and balanced performance against various types of attacks indicate the potential application of this model in real-world situations. This model shows promising performance in detecting various network traffic anomalies, providing important insights related to its potential practical use. Further research is still needed to enhance resilience against evolving DDoS attack tactics and to address potential limitations that may exist.
Authors: Zukipli Zukipli, Aprila Kusuma Riskila, Eko Saputra, Muh Syaikhurrahman, Bimo Prasetyo
Institution: Universitas Bumigora
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4414000284