Developing realistic distributed denial of service (DDoS) attack dataset and taxonomy
Distributed Denial of Service (DDoS) attack is a menace to network security that aims at
exhausting the target networks with malicious traffic. Although many statistical methods have …
exhausting the target networks with malicious traffic. Although many statistical methods have …
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 …
Evaluation of classification algorithms for distributed denial of service attack detection
M Gohil, S Kumar - 2020 IEEE Third International Conference …, 2020 - ieeexplore.ieee.org
Distributed Denial of Service (DDoS) attacks aims exhausting the target network with
malicious traffic, which is a threat to the availability of the service. Many detection systems …
malicious traffic, which is a threat to the availability of the service. Many detection systems …
[HTML][HTML] A hybrid machine learning approach for detecting unprecedented DDoS attacks
Abstract Service availability plays a vital role on computer networks, against which
Distributed Denial of Service (DDoS) attacks are an increasingly growing threat each year …
Distributed Denial of Service (DDoS) attacks are an increasingly growing threat each year …
[HTML][HTML] Towards effective detection of recent DDoS attacks: A deep learning approach
Distributed Denial of Service (DDoS) is a predominant threat to the availability of online
services due to their size and frequency. However, developing an effective security …
services due to their size and frequency. However, developing an effective security …
[HTML][HTML] A dynamic MLP-based DDoS attack detection method using feature selection and feedback
M Wang, Y Lu, J Qin - Computers & Security, 2020 - Elsevier
Abstract Distributed Denial of Service (DDoS) attack is a stubborn network security problem.
Various machine learning-based methods have been proposed to detect such attacks …
Various machine learning-based methods have been proposed to detect such attacks …
A review of detection approaches for distributed denial of service attacks
P Kaur, M Kumar, A Bhandari - Systems Science & Control …, 2017 - Taylor & Francis
ABSTRACT Distributed Denial of Service (DDoS) attacks are the intimidation trials on the
Internet that depletes the network bandwidth or exhausts the victim's resources …
Internet that depletes the network bandwidth or exhausts the victim's resources …
Deepdetect: detection of distributed denial of service attacks using deep learning
At the advent of advanced wireless technology and contemporary computing paradigms,
Distributed Denial of Service (DDoS) attacks on Web-based services have not only …
Distributed Denial of Service (DDoS) attacks on Web-based services have not only …
LSTM-BA: DDoS detection approach combining LSTM and Bayes
Y Li, Y Lu - … international conference on advanced cloud and …, 2019 - ieeexplore.ieee.org
The development of cyberspace brings both opportunities and threats, among which
Distributed Denial of Service (DDoS) is one of the most destructive attacks. A mass of DDoS …
Distributed Denial of Service (DDoS) is one of the most destructive attacks. A mass of DDoS …
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 …