NetSpirit: A smart collaborative learning framework for DDoS attack detection
Facing one of the most common threats to Internet security, the existing traffic-driven
distributed denial of service (DDoS) defense schemes mainly focus on establishing more …
distributed denial of service (DDoS) defense schemes mainly focus on establishing more …
Information-theoretic ensemble learning for ddos detection with adaptive boosting
DDoS (Distributed Denial of Service) attacks pose a serious threat to the Internet as they use
large numbers of zombie hosts to forward massive numbers of packets to the target host …
large numbers of zombie hosts to forward massive numbers of packets to the target host …
[HTML][HTML] FTG-Net-E: A hierarchical ensemble graph neural network for DDoS attack detection
Abstract Distributed Denial-of-Service (DDoS) attacks are a major threat to computer
networks. These attacks can be carried out by flooding a network with malicious traffic …
networks. These attacks can be carried out by flooding a network with malicious traffic …
Ae-mlp: A hybrid deep learning approach for ddos detection and classification
Distributed Denial-of-Service (DDoS) attacks are increasing as the demand for Internet
connectivity massively grows in recent years. Conventional shallow machine learning-based …
connectivity massively grows in recent years. Conventional shallow machine learning-based …
LUCID: A practical, lightweight deep learning solution for DDoS attack detection
R Doriguzzi-Corin, S Millar… - … on Network and …, 2020 - ieeexplore.ieee.org
Distributed Denial of Service (DDoS) attacks are one of the most harmful threats in today's
Internet, disrupting the availability of essential services. The challenge of DDoS detection is …
Internet, disrupting the availability of essential services. The challenge of DDoS detection is …
Ddos attack detection based on cnn and federated learning
D Lv, X Cheng, J Zhang, W Zhang… - … on Advanced Cloud …, 2022 - ieeexplore.ieee.org
Distributed Denial of Service (DDoS) attack, which seriously affects the availability of the
Internet, is one of the most dangerous network attacks. Machine learning is widely used in …
Internet, is one of the most dangerous network attacks. Machine learning is widely used in …
A machine learning classification model using random forest for detecting DDoS attacks
TS Chu, W Si, S Simoff… - … Symposium on Networks …, 2022 - ieeexplore.ieee.org
Distributed Denial of Service (DDoS) attacks exhaust the resources of network services by
generating a huge volume of network traffic. They constitute a primary threat to the current …
generating a huge volume of network traffic. They constitute a primary threat to the current …
FLDDoS: DDoS attack detection model based on federated learning
J Zhang, P Yu, L Qi, S Liu, H Zhang… - 2021 IEEE 20th …, 2021 - ieeexplore.ieee.org
Recently, DDoS attack has developed rapidly and become one of the most important threats
to the Internet. Traditional machine learning and deep learning methods can-not train a …
to the Internet. Traditional machine learning and deep learning methods can-not train a …
Toward explainable and adaptable detection and classification of distributed denial-of-service attacks
Y Feng, J Li - Deployable Machine Learning for Security Defense …, 2020 - Springer
By attacking (eg, flooding) the bandwidth or resources of a victim (eg, a web server) on the
Internet from multiple compromised systems (eg, a botnet), distributed Denial-of-Service …
Internet from multiple compromised systems (eg, a botnet), distributed Denial-of-Service …
[PDF][PDF] Lucid: A practical, lightweight deep learning solution for ddos attack detection
R Doriguzzi-Corinα, S Millarβ, S Scott-Haywardβ… - 2019 - pureadmin.qub.ac.uk
Distributed Denial of Service (DDoS) attacks are one of the most harmful threats in today's
Internet, disrupting the availability of essential services. The challenge of DDoS detection is …
Internet, disrupting the availability of essential services. The challenge of DDoS detection is …