[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 …

Graphddos: Effective ddos attack detection using graph neural networks

Y Li, R Li, Z Zhou, J Guo, W Yang… - 2022 IEEE 25th …, 2022 - ieeexplore.ieee.org
Distributed Denial of Service (DDoS) attacks have occurred frequently in recent years,
causing massive damage. It is critical to detect DDoS attacks fast and accurately. Previous …

Deep Ensemble Learning with Pruning for DDoS Attack Detection in IoT Networks

MF Saiyed, I Al-Anbagi - IEEE Transactions on Machine …, 2024 - ieeexplore.ieee.org
The upsurge of Internet of Things (IoT) devices has increased their vulnerability to
Distributed Denial of Service (DDoS) attacks. DDoS attacks have evolved into complex multi …

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 …

Empirical performance evaluation of machine learning based DDoS attack detections

BS Tran, TH Ho, TX Do, KH Le - Recent Advances in Internet of Things and …, 2022 - Springer
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 …

A lightweight residual networks framework for DDoS attack classification based on federated learning

Q Tian, C Guang, C Wenchao… - IEEE INFOCOM 2021-IEEE …, 2021 - ieeexplore.ieee.org
With the development of network technology, more and more protocols and devices are
used in DDoS reflection and exploitation attacks. Different DDoS attacks often require …

Enhancing DDoS Attack Detection via Blending Ensemble Learning

CRJ Amalraj, PGG Madhusankha - 2023 8th International …, 2023 - ieeexplore.ieee.org
This research focuses on identifying DDoS attacks using an ensemble learning approach
that incorporates blending techniques. We developed an innovative methodology by …

[PDF][PDF] Exploring Unsupervised Learning with Clustering and Deep Autoencoder to Detect DDoS Attack

X Zhang, J Gai, Z Ma, J Zhao, H Ma, F He, T Ju - Journal of Computers, 2022 - csroc.org.tw
With the proliferation of services available on the Internet, network attacks have become one
of the serious issues. The distributed denial of service (DDoS) attack is such a devastating …

CoWatch: Collaborative prediction of DDoS attacks in edge computing with distributed SDN

H Zhou, X Jia, J Shu, L Zhou - 2021 IEEE Global …, 2021 - ieeexplore.ieee.org
With the development of Edge Computing (EC), security issues have raised concerns. Due
to the unusual vulnera-bility of EC servers and the distributed nature of attack sources, it is a …

DDoS Attack Detection in a Real Urban IoT Environment Using Federated Deep Learning

K Ahmadi, R Javidan - … on Cyber Security and Resilience (CSR …, 2023 - ieeexplore.ieee.org
today, alongside the opportunities provided by Internet of Things (IoT), Distributed Denial of
Service (DDoS) attacks are one of the most significant attacks that target the overall …