An Effective Classification of DDoS Attacks in a Distributed Network by Adopting Hierarchical Machine Learning and Hyperparameters Optimization Techniques
Data privacy is essential in the financial sector to protect client’s sensitive information, prevent financial fraud, ensure regulatory compliance, and safeguard intellectual property. It has become a challenging task due to the increase in usage of the internet and digital transactions....
Main Authors: | Sandeep Dasari, Rajesh Kaluri |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2024-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10387439/ |
Similar Items
-
Predicting DoS and DDoS attacks in network security scenarios using a hybrid deep learning model
by: Al-zubidi Azhar F., et al.
Published: (2024-04-01) -
Basic Hyperparameters Tuning Methods for Classification Algorithms
by: Claudia ANTAL-VAIDA
Published: (2021-01-01) -
Physical Assessment of an SDN-Based Security Framework for DDoS Attack Mitigation: Introducing the SDN-SlowRate-DDoS Dataset
by: Noe M. Yungaicela-Naula, et al.
Published: (2023-01-01) -
Hybrid Deep Learning Approach for Automatic DoS/DDoS Attacks Detection in Software-Defined Networks
by: Hani Elubeyd, et al.
Published: (2023-03-01) -
Supervised learning‐based DDoS attacks detection: Tuning hyperparameters
by: Meejoung Kim
Published: (2019-09-01)