Fine-tuning a pre-trained ResNet50 model to detect distributed denial of service attack
Ahmad Sanmorino,
Hendra Di Kesuma
Abstract:Distributed denial-of-service (DDoS) attacks pose a significant risk to the dependability and consistency of network services. The utilization of deep learning (DL) models has displayed encouraging outcomes in the identification of DDoS attacks. Nevertheless, crafting a precise DL model necessitates an extensive volume of labeled data and substantial computational capabilities. Within this piece, we introduce a technique to enhance a pre-trained DL model for the identification of DDoS attacks. Our strategy’s e… Show more
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