|
|
|
| DDoS Attack Detection Method Based on Multi-scale Convolutional Neural Network |
|
1. Institute of Disaster Prevention
2. Mineral Resources Information Center of CMGB
|
|
|
|
|
Abstract In recent years, network security is facing increasing challenges, among which Distributed Denial of Service
(DDoS) attack is a common form of network threats. In order to deal with this challenge, this paper proposes a DDoS
attack detection method based on Multi-scale Convolutional Neural Network (MSCNN). The model is trained on the
CICDDoS2019day1 dataset, and the model detection performance is tested on the CICDDoS2019day2 dataset. By using
MSCNN to predict and classify network tra?c, DDoS attacks can be e?ectively identi?ed and false positive rate can be
reduced. Experiments show that the MSCNN method is superior to DNN, CNN and LSTM in terms of accuracy, recall and
F1 score performance metrics.
|
|
Published: 01 November 2024
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
