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 …
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 …
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 …
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 …
Feature selection evaluation towards a lightweight deep learning DDoS detector
Today's networks and services undoubtedly require a high level of protection from cyber
threats and attacks. State-of-the-art solutions that implement Machine Learning (ML) have …
threats and attacks. State-of-the-art solutions that implement Machine Learning (ML) have …
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 …
Ddos attacks detection with autoencoder
Although many distributed denial of service (DDoS) attacks detection algorithms have been
proposed and even some of them have claimed high detection accuracy, DDoS attacks are …
proposed and even some of them have claimed high detection accuracy, DDoS attacks are …
Evaluating ML-based DDoS detection with grid search hyperparameter optimization
Distributed Denial of Service (DDoS) attacks disrupt global network services by mainly
overwhelming the victim host with requests originating from multiple traffic sources. DDoS …
overwhelming the victim host with requests originating from multiple traffic sources. DDoS …
Improving ddos attack detection leveraging a multi-aspect ensemble feature selection
DDoS attack detection is crucial in computer networks to meet the reliability and accessibility
requirements of online services. The ability of machine learning to discriminate between …
requirements of online services. The ability of machine learning to discriminate between …