AI in the Detection and Prevention of Distributed Denial of Service (DDoS) Attacks
Distributed Denial of Service (DDoS) attacks are malicious attacks that aim to disrupt the normal flow of traffic to the targeted server or network by manipulating the server’s infrastructure with overflowing internet traffic. This study aims to investigate several artificial intelligence (AI) models and utilise them in the DDoS detection system.
openaire +1 more source
A hybrid machine learning approach for detecting DDoS attacks in software-defined networks. [PDF]
Mahar IA +5 more
europepmc +1 more source
Dynamic graph neural network-based framework to increase detection accuracy in SDN under DDOS. [PDF]
Kalafy SAA, Pashazadeh S, Salehpour P.
europepmc +1 more source
Modelling of hybrid deep learning framework with recursive feature elimination for distributed denial of service attack detection systems. [PDF]
Alkhliwi S.
europepmc +1 more source
Cloud-based DDoS detection using hybrid feature selection with deep reinforcement learning (DRL). [PDF]
Satpathy S, Tripathy U, Swain PK.
europepmc +1 more source
Quantum-resilient cross-trust evaluation for zero trust 5G security. [PDF]
Jeysuriya K, Renjith PN, Sudhakaran G.
europepmc +1 more source
Datasets for distributed denial-of-service detection in healthcare internet of things environments. [PDF]
Akhi M, Eising C, Dhirani LL.
europepmc +1 more source
Nature-inspired swarm optimization paradigms for securing semantic web frameworks against DDoS attacks: a computational approach. [PDF]
Ganguli C +3 more
europepmc +1 more source
Mitigating distributed denial of service attacks using attribute subset selection with temporal convolutional networks. [PDF]
Alamro H +7 more
europepmc +1 more source
Mitigating distributed denial of service-based cyberattack in federated computing framework using deep reinforcement learning with frilled lizard algorithm. [PDF]
Maghrabi LA +6 more
europepmc +1 more source

