DDoS Attacks Detection With Deep Learning Approach Using Convolutional Neural Network
The detection system of DDoS (Distributed Denial-of-Service) attacks aims to enhance network security across all facets of internet technology utilization. One is at SPKLU, which stands for Public Electric Vehicle Charging Station. The research employed a deep learning approach utilizing a Convoluti...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Politeknik Negeri Batam
2024-08-01
|
| Series: | Journal of Applied Informatics and Computing |
| Subjects: | |
| Online Access: | https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/8242 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850169082097172480 |
|---|---|
| author | Rafiq Amalul Widodo Mera Kartika Delimayanti Asri Wulandari |
| author_facet | Rafiq Amalul Widodo Mera Kartika Delimayanti Asri Wulandari |
| author_sort | Rafiq Amalul Widodo |
| collection | DOAJ |
| description | The detection system of DDoS (Distributed Denial-of-Service) attacks aims to enhance network security across all facets of internet technology utilization. One is at SPKLU, which stands for Public Electric Vehicle Charging Station. The research employed a deep learning approach utilizing a Convolutional Neural Network (CNN) on a publicly available dataset. Based on our study and analysis, CNN has a precision rate of 95%. Its high accuracy and balanced performance across diverse attack types indicate the model's practical application in real-life situations. The model demonstrates promising performance in detecting different network traffic anomalies, offering significant insight into its potential for practical use. Further investigation is necessary to strengthen the resilience of DDoS assault tactics against emerging dangers and to tackle any potential constraints. |
| format | Article |
| id | doaj-art-645cd23b8d7a4e2e84e144ee5c325851 |
| institution | OA Journals |
| issn | 2548-6861 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | Politeknik Negeri Batam |
| record_format | Article |
| series | Journal of Applied Informatics and Computing |
| spelling | doaj-art-645cd23b8d7a4e2e84e144ee5c3258512025-08-20T02:20:49ZengPoliteknik Negeri BatamJournal of Applied Informatics and Computing2548-68612024-08-018223524010.30871/jaic.v8i2.82428242DDoS Attacks Detection With Deep Learning Approach Using Convolutional Neural NetworkRafiq Amalul Widodo0Mera Kartika Delimayanti1Asri Wulandari2Politeknik Negeri JakartaPoliteknik Negeri JakartaPoliteknik Negeri JakartaThe detection system of DDoS (Distributed Denial-of-Service) attacks aims to enhance network security across all facets of internet technology utilization. One is at SPKLU, which stands for Public Electric Vehicle Charging Station. The research employed a deep learning approach utilizing a Convolutional Neural Network (CNN) on a publicly available dataset. Based on our study and analysis, CNN has a precision rate of 95%. Its high accuracy and balanced performance across diverse attack types indicate the model's practical application in real-life situations. The model demonstrates promising performance in detecting different network traffic anomalies, offering significant insight into its potential for practical use. Further investigation is necessary to strengthen the resilience of DDoS assault tactics against emerging dangers and to tackle any potential constraints.https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/8242convolutional neural networkddos attacksdeep learningelectric vehicle |
| spellingShingle | Rafiq Amalul Widodo Mera Kartika Delimayanti Asri Wulandari DDoS Attacks Detection With Deep Learning Approach Using Convolutional Neural Network Journal of Applied Informatics and Computing convolutional neural network ddos attacks deep learning electric vehicle |
| title | DDoS Attacks Detection With Deep Learning Approach Using Convolutional Neural Network |
| title_full | DDoS Attacks Detection With Deep Learning Approach Using Convolutional Neural Network |
| title_fullStr | DDoS Attacks Detection With Deep Learning Approach Using Convolutional Neural Network |
| title_full_unstemmed | DDoS Attacks Detection With Deep Learning Approach Using Convolutional Neural Network |
| title_short | DDoS Attacks Detection With Deep Learning Approach Using Convolutional Neural Network |
| title_sort | ddos attacks detection with deep learning approach using convolutional neural network |
| topic | convolutional neural network ddos attacks deep learning electric vehicle |
| url | https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/8242 |
| work_keys_str_mv | AT rafiqamalulwidodo ddosattacksdetectionwithdeeplearningapproachusingconvolutionalneuralnetwork AT merakartikadelimayanti ddosattacksdetectionwithdeeplearningapproachusingconvolutionalneuralnetwork AT asriwulandari ddosattacksdetectionwithdeeplearningapproachusingconvolutionalneuralnetwork |