Detection of unknown ddos attacks with deep learning and gaussian mixture model

CS Shieh, WW Lin, TT Nguyen, CH Chen, MF Horng… - Applied Sciences, 2021 - mdpi.com
DDoS (Distributed Denial of Service) attacks have become a pressing threat to the security
and integrity of computer networks and information systems, which are indispensable …

Efficient detection of DDoS attacks using a hybrid deep learning model with improved feature selection

D Alghazzawi, O Bamasag, H Ullah, MZ Asghar - Applied Sciences, 2021 - mdpi.com
DDoS (Distributed Denial of Service) attacks have now become a serious risk to the integrity
and confidentiality of computer networks and systems, which are essential assets in today's …

SDN-based architecture for transport and application layer DDoS attack detection by using machine and deep learning

NM Yungaicela-Naula, C Vargas-Rosales… - IEEE …, 2021 - ieeexplore.ieee.org
Distributed Denial of Service (DDoS) attacks represent the most common and critical attacks
targeting conventional and new generation networks, such as the Internet of Things (IoT) …

DDoS detection using deep learning

D Kumar, RK Pateriya, RK Gupta, V Dehalwar… - Procedia Computer …, 2023 - Elsevier
The network's infrastructure becomes more vulnerable to cyber-attacks as the number of
services offered through the internet expands. The complexity of" Distributed Denial-of …

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 …

Ae-mlp: A hybrid deep learning approach for ddos detection and classification

Y Wei, J Jang-Jaccard, F Sabrina, A Singh, W Xu… - IEEE …, 2021 - ieeexplore.ieee.org
Distributed Denial-of-Service (DDoS) attacks are increasing as the demand for Internet
connectivity massively grows in recent years. Conventional shallow machine learning-based …

A holistic approach for detecting DDoS attacks by using ensemble unsupervised machine learning

S Das, D Venugopal, S Shiva - … : Proceedings of the 2020 Future of …, 2020 - Springer
Abstract Distributed Denial of Service (DDoS) has been the most prominent attack in cyber-
physical system over the last decade. Defending against DDoS attack is not only …

A long short-term memory enabled framework for DDoS detection

X Liang, T Znati - 2019 IEEE global communications …, 2019 - ieeexplore.ieee.org
The proliferation of attack-for-hire services, coupled with the advent of Internet of Things
(IoT)-enabled botnets, is driving the increase of the frequency and intensity of Distributed …

Software-defined DDoS detection with information entropy analysis and optimized deep learning

Y Liu, T Zhi, M Shen, L Wang, Y Li, M Wan - Future Generation Computer …, 2022 - Elsevier
Abstract Software Defined Networking (SDN) decouples the control plane and the data
plane and solves the difficulty of new services deployment. However, the threat of a single …

Real-time DDoS attack detection system using big data approach

MJ Awan, U Farooq, HMA Babar, A Yasin, H Nobanee… - Sustainability, 2021 - mdpi.com
Currently, the Distributed Denial of Service (DDoS) attack has become rampant, and shows
up in various shapes and patterns, therefore it is not easy to detect and solve with previous …