Fuzzy logic-based DDoS attacks and network traffic anomaly detection methods: Classification, overview, and future perspectives

D Javaheri, S Gorgin, JA Lee, M Masdari - Information Sciences, 2023 - Elsevier
Nowadays, cybersecurity challenges and their ever-growing complexity are the main
concerns for various information technology-driven organizations and companies. Although …

Machine learning techniques in emerging cloud computing integrated paradigms: A survey and taxonomy

D Soni, N Kumar - Journal of Network and Computer Applications, 2022 - Elsevier
Cloud computing offers Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and
Software as a Service (SaaS) to provide compute, network, and storage capabilities to the …

Adversarial Deep Learning approach detection and defense against DDoS attacks in SDN environments

MP Novaes, LF Carvalho, J Lloret… - Future Generation …, 2021 - Elsevier
Over the last few years, Software Defined Networking (SDN) paradigm has become an
emerging architecture to design future networks and to meet new application demands. SDN …

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) …

Detection and mitigation of DDoS attacks in SDN: A comprehensive review, research challenges and future directions

J Singh, S Behal - Computer Science Review, 2020 - Elsevier
Many security solutions have been proposed in the past to protect Internet architecture from
a diversity of malware. However, the security of the Internet and its applications is still an …

DIDDOS: An approach for detection and identification of Distributed Denial of Service (DDoS) cyberattacks using Gated Recurrent Units (GRU)

S Ur Rehman, M Khaliq, SI Imtiaz, A Rasool… - Future Generation …, 2021 - Elsevier
Abstract Distributed Denial of Service (DDoS) attacks can put the communication networks
in instability by throwing malicious traffic and requests in bulk over the network. Computer …

MR‐DCAE: Manifold regularization‐based deep convolutional autoencoder for unauthorized broadcasting identification

Q Zheng, P Zhao, D Zhang… - International Journal of …, 2021 - Wiley Online Library
Nowadays, radio broadcasting plays an important role in people's daily life. However,
unauthorized broadcasting stations may seriously interfere with normal broadcastings and …

A long short-term memory (LSTM)-based distributed denial of service (DDoS) detection and defense system design in public cloud network environment

H Aydın, Z Orman, MA Aydın - Computers & Security, 2022 - Elsevier
The fact that cloud systems are under the increasing risks of cyber attacks has made the
phenomenon of information security first a need and then a necessity for these systems …

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 …

A generalized machine learning model for DDoS attacks detection using hybrid feature selection and hyperparameter tuning

RK Batchu, H Seetha - Computer Networks, 2021 - Elsevier
In the digital era, the usage of network-connected devices is rapidly growing which leads to
an increase in cyberattacks. Among them, Distributed Denial of Service (DDoS) attacks are …