Application layer DDoS attack detection using cluster with label based on sparse vector decomposition and rhythm matching

2015 ◽  
Vol 8 (17) ◽  
pp. 3111-3120 ◽  
Author(s):  
Qin Liao ◽  
Hong Li ◽  
Songlin Kang ◽  
Chuchu Liu
Keyword(s):  
Attack Detection ◽  
Application Layer ◽  
Ddos Attack ◽  
Vector Decomposition ◽  
Sparse Vector ◽  
Ddos Attack Detection

scholarly journals WEB DDoS Attack Detection Method Based on Semisupervised Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiang Yu ◽  
Wenchao Yu ◽  
Shudong Li ◽  
Xianfei Yang ◽  
Ying Chen ◽  
...  
Keyword(s):  
Web Application ◽  
False Positive Rate ◽  
Semisupervised Learning ◽  
Attack Detection ◽  
Application Layer ◽  
Ddos Attack ◽  
Web Attacks ◽  
Positive Rate ◽  
Ddos Attack Detection ◽  
The Web

Since the services on the Internet are becoming increasingly abundant, all walks of life are inextricably linked with the Internet. Simultaneously, the Internet’s WEB attacks have never stopped. Relative to other common WEB attacks, WEB DDoS (distributed denial of service) will cause serious damage to the availability of the target network or system resources in a short period of time. At present, most researches are centered around machine learning-related DDoS attack detection algorithms. According to previous studies, unsupervised methods generally have a high false positive rate, while supervisory methods cannot handle large amount of network traffic data, and the performance is often limited by noise and irrelevant data. Therefore, this paper proposes a semisupervised learning detection model combining spectral clustering and random forest to detect the DDoS attack of the WEB application layer and compares it with other existing detection schemes to verify the semisupervised learning model proposed in this paper. While ensuring a low false positive rate, there is a certain improvement in the detection rate, which is more suitable for the WEB application layer DDoS attack detection.

scholarly journals SDN-Based Architecture for Transport and Application Layer DDoS Attack Detection by Using Machine and Deep Learning

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Noe M. Yungaicela-Naula ◽  
Cesar Vargas-Rosales ◽  
Jesus Arturo Perez-Diaz
Keyword(s):  
Deep Learning ◽  
Attack Detection ◽  
Application Layer ◽  
Ddos Attack ◽  
Ddos Attack Detection

scholarly journals Entropy-Based Application Layer DDoS Attack Detection Using Artificial Neural Networks

Entropy ◽  
2016 ◽  
Vol 18 (10) ◽  
pp. 350 ◽  
Author(s):  
Khundrakpam Johnson Singh ◽  
Khelchandra Thongam ◽  
Tanmay De
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Attack Detection ◽  
Application Layer ◽  
Ddos Attack ◽  
Artificial Neural ◽  
Ddos Attack Detection
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