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 …
Applying supervised machine learning techniques to detect DDoS attacks
AA Najar, SM Naik - 2022 2nd Asian Conference on Innovation …, 2022 - ieeexplore.ieee.org
Distributed Denial of Service (DDOS) attack is one of the most devastating attacks, since it
disrupts the performance of fundamental services provided by many companies on the …
disrupts the performance of fundamental services provided by many companies on the …
DDoS attacks detection using machine learning algorithms
Q Li, L Meng, Y Zhang, J Yan - … 2018, Shanghai, China, September 20–21 …, 2019 - Springer
A distributed denial-of-service (DDoS) attack is a malicious attempt to disrupt normal traffic of
a targeted server, service or network by overwhelming the target or its surrounding …
a targeted server, service or network by overwhelming the target or its surrounding …
Empirical performance evaluation of machine learning based DDoS attack detections
A distributed denial-of-service attack (DDoS) is a critical attack-type that strongly damages
the Quality of Service (QoE). Although various novel security technologies have been …
the Quality of Service (QoE). Although various novel security technologies have been …
A novel CNN‐based approach for detection and classification of DDoS attacks
Among the recent network security issues, Distributed Denial of Service (DDoS) attack is
one of the most dangerous threats in today's cyberspace that can disrupt essential services …
one of the most dangerous threats in today's cyberspace that can disrupt essential services …
Efficient Machine Learning Algorithms for DDoS Attack Detection
AM Al-Eryani, E Hossny… - 2024 6th International …, 2024 - ieeexplore.ieee.org
Distributed Denial of Service (DDoS) attack is a widely spread attack that posing a major
threat to organizations dependent on online services. DDoS attacks aim to disrupt services …
threat to organizations dependent on online services. DDoS attacks aim to disrupt services …
Detection of ddos attacks based on dense neural networks, autoencoders and pearson correlation coefficient
J Li - 2020 - dalspace.library.dal.ca
Distributed Denial of Service (DDoS) is a set of frequent cyber attacks used against public
servers. Because DDoS attacks can be launched remotely and reflected by legit-imated …
servers. Because DDoS attacks can be launched remotely and reflected by legit-imated …
An Autoencoder-Based Approach for DDoS Attack Detection Using Semi-Supervised Learning
T Fardusy, S Afrin, IJ Sraboni… - … Conference on Next …, 2023 - ieeexplore.ieee.org
A Distributed Denial of Service (DDoS) attack is a malicious cyber-attack strategy that seeks
to disrupt normal traffic to a specific server by overwhelming it with an excessive amount of …
to disrupt normal traffic to a specific server by overwhelming it with an excessive amount of …
[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 …
Enhanced DDoS Detection using Machine Learning
R Pandey, M Pandey, A Nazarov - 2023 6th International …, 2023 - ieeexplore.ieee.org
The rapid growth of internet population poses a serious challenge to the security of internet
resources. The security is directly affected by the hits of Denial of Services (DoS) attack …
resources. The security is directly affected by the hits of Denial of Services (DoS) attack …