Deep Learning Models Comparison in binary context for DDoS Attack ...
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Deep Learning-Based Approach for Detecting DDoS Attack on Software-Defined Networking Controller · Hybrid Deep Learning Approach for Automatic Dos/Ddos Attacks ...
Deep Learning Models Comparison in binary context for DDoS Attack Detection in Software-Defined Network. Ameur Salem Zaidoun, Zied Lachiri - 2024. Abstract
Oct 22, 2024 · Attack Resilience: Ensuring the network remains resilient and performs well under potential security attacks, such as distributed denial of ...
Feb 27, 2023 · ... ddos attack ... Detecting ddos attacks in software-defined networks through feature selection methods and machine learning models.
Mar 15, 2024 · DDoS attacks can bring disastrous consequences to SDN networks and controllers, so the attack detection model needs to consider not only the ...
Feb 17, 2023 · DDoS attacks, it may be difficult to differentiate normal burst traffic from attack traffic. One way to increase accuracy is to establish a DDoS ...
Jun 29, 2024 · The model improved SDN security by rapidly and accurately detecting attacks. FMDADM, an SDN-based attack mitigation and detection system for IoT ...
Feb 27, 2024 · This comparison is instrumental in elucidating the relative strengths and weaknesses of each model in the context of DDoS attack detection ( ...
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Today, the definition of a DDoS attack gets more and more complicated as cybercriminals utilize combinations of high-volume attacks. It is becoming more ...
Aug 19, 2023 · The DDoS detection problem is a binary classification problem in which the observed traffic is either normal or attack traffic. Moreover, ...