Spotting anomalies at the edge: Outlier exposure-based cross-silo federated learning for ddos detection
V Pourahmadi, HA Alameddine… - … on Dependable and …, 2022 - ieeexplore.ieee.org
Distributed Denial-of-Service (DDoS) attacks are expected to continue plaguing service
availability in emerging networks which rely on distributed edge clouds to offer critical …
availability in emerging networks which rely on distributed edge clouds to offer critical …
A Novel Supervised Deep Learning Solution to Detect Distributed Denial of Service (DDoS) attacks on Edge Systems using Convolutional Neural Networks (CNN)
V Ramanathan, K Mahadevan, S Dua - arXiv preprint arXiv:2309.05646, 2023 - arxiv.org
Cybersecurity attacks are becoming increasingly sophisticated and pose a growing threat to
individuals, and private and public sectors. Distributed Denial of Service attacks are one of …
individuals, and private and public sectors. Distributed Denial of Service attacks are one of …
Correlation-aware neural networks for DDOS attack detection in IoT systems
We present a comprehensive study on applying machine learning to detect distributed
Denial of service (DDoS) attacks using large-scale Internet of Things (IoT) systems. While …
Denial of service (DDoS) attacks using large-scale Internet of Things (IoT) systems. While …
Efficient DDoS Attack Detection using Machine Learning Techniques
F Nazarudeen, S Sundar - 2022 IEEE International Power and …, 2022 - ieeexplore.ieee.org
Distributed Denial-of-Service (DDoS) attacks are deliberate attempts to interrupt the regular
traffic of a specific server, network, organization, by flooding the victim or its neighbouring …
traffic of a specific server, network, organization, by flooding the victim or its neighbouring …
Experimenting Ensemble Machine Learning for DDoS Classification: Timely Detection of DDoS Using Large Scale Dataset
H Amaad, H Mughal - 2023 4th International Conference on …, 2023 - ieeexplore.ieee.org
The rapid expansion of the internet has connected the world with a single network. Every
network is the victim of a hacker and can be attacked by finding its vulnerabilities. Distributed …
network is the victim of a hacker and can be attacked by finding its vulnerabilities. Distributed …
DL-2P-DDoSADF: Deep learning-based two-phase DDoS attack detection framework
M Mittal, K Kumar, S Behal - Journal of Information Security and …, 2023 - Elsevier
In today's tech-driven world, while Internet-based applications drive social progress, their
architectural weaknesses, inadequate security measures, lack of network segmentation …
architectural weaknesses, inadequate security measures, lack of network segmentation …
TDSC: Two-stage DDoS detection and defense system based on clustering
S Wei, Y Ding, X Han - 2017 47th Annual IEEE/IFIP …, 2017 - ieeexplore.ieee.org
Distributed Denial-of-Service (DDoS) attack continues to be one of the most serious
problems in the Internet. Without advance warning, DDoS attack can knock down the …
problems in the Internet. Without advance warning, DDoS attack can knock down the …
[PDF][PDF] Analysis Distributed Denial-of-Service Attack Deploy Deep Learning Techniques
S Qureshi, J He, S Tunio, N Zhu, F Ullah… - … Journal of Network …, 2023 - ijns.jalaxy.com.tw
Network devices are essential to connect nodes and users on any given network. Network
devices perform the additional task of protecting services and users from known and …
devices perform the additional task of protecting services and users from known and …
A flexible SDN-based framework for slow-rate DDoS attack mitigation by using deep reinforcement learning
NM Yungaicela-Naula, C Vargas-Rosales… - Journal of network and …, 2022 - Elsevier
Abstract Distributed Denial-of-Service (DDoS) attacks are difficult to mitigate with existing
defense tools. Fortunately, it has been demonstrated that Software-Defined Networking …
defense tools. Fortunately, it has been demonstrated that Software-Defined Networking …