FedDDoS: An efficient federated learning-based DDoS attacks classification in SDN-enabled IIoT networks
Independent distribution systems are made possible by Industry 4.0, and these systems
produce heterogeneous data that is vulnerable to cyberattacks. The Distributed Denial of …
produce heterogeneous data that is vulnerable to cyberattacks. The Distributed Denial of …
Evaluating ML-based DDoS detection with grid search hyperparameter optimization
Distributed Denial of Service (DDoS) attacks disrupt global network services by mainly
overwhelming the victim host with requests originating from multiple traffic sources. DDoS …
overwhelming the victim host with requests originating from multiple traffic sources. DDoS …
Towards effective detection of recent DDoS attacks: A deep learning approach
I Ortet Lopes, D Zou, FA Ruambo… - Security and …, 2021 - Wiley Online Library
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 …
E-SDNN: encoder-stacked deep neural networks for DDOS attack detection
E Benmohamed, A Thaljaoui, S Elkhediri… - Neural Computing and …, 2024 - Springer
The increasing reliance on internet-based services has heightened the vulnerability of
network infrastructure to cyberattacks, particularly distributed denial of service (DDoS) …
network infrastructure to cyberattacks, particularly distributed denial of service (DDoS) …
An intelligent behavioral-based DDOS attack detection method using adaptive time intervals
A Shamekhi, P Shamsinejad Babaki… - Peer-to-Peer Networking …, 2024 - Springer
Dealing with network attacks is becoming more uphill as we go further due to the complexity
of computer networks. Among all the network attacks, DDoS attacks are widespread and …
of computer networks. Among all the network attacks, DDoS attacks are widespread and …
LSTM-based collaborative source-side DDoS attack detection
As denial of service attacks become more sophisticated, the source-side detection
techniques are being studied to solve the limitations of target-side detection techniques such …
techniques are being studied to solve the limitations of target-side detection techniques such …
An asynchronous federated learning arbitration model for low-rate ddos attack detection
Z Liu, C Guo, D Liu, X Yin - IEEE Access, 2023 - ieeexplore.ieee.org
Low-rate Distributed Denial of Service (LDDoS) attacks have been one of the most notorious
network security threats, which use periodic slight multi-variate time series pulse flows to …
network security threats, which use periodic slight multi-variate time series pulse flows to …
Enhancing DDoS attack detection and mitigation in SDN using an ensemble online machine learning model
Software Defined Networks (SDN) offer dynamic reconfigurability and scalability,
revolutionizing traditional networking. However, countering Distributed Denial of Service …
revolutionizing traditional networking. However, countering Distributed Denial of Service …
DDoS attacks and machine‐learning‐based detection methods: A survey and taxonomy
Distributed denial of service (DDoS) attacks represent a significant cybersecurity challenge,
posing a critical risk to computer networks. Developing an effective defense mechanism …
posing a critical risk to computer networks. Developing an effective defense mechanism …
Federated Learning for Decentralized DDoS Attack Detection in IoT Networks
Y Alhasawi, S Alghamdi - IEEE Access, 2024 - ieeexplore.ieee.org
In the ever-expanding domain of Internet of Things (IoT) networks, Distributed Denial of
Service (DDoS) attacks represent a significant challenge, compromising the reliability of …
Service (DDoS) attacks represent a significant challenge, compromising the reliability of …