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This Convolutional neural networks and CNN combined with (CNN + LSTM) are used to assess the prediction accuracy of network denial-of-service attacks. These models are used to improve the accuracy of online forecasts using datasets of Network DDOS attacks. Each of the 3100 records in the datasets has 420 rows and 25 columns. MedCalc is used to estimate a sample size of 10 per group with an alpha of 0.05 and a power of 0.8. The performance of the models is used to evaluate how accurate the Network DDOS attack predictions are. The findings show that when it comes to outcome prediction, the convolutional neural network (92.95%) outperforms CNN + LSTM (86.33%). This illustrates the findings’ statistical significance. Compared to convolutional neural networks, CNN + LSTM predicts network DDOS attacks with a lower average accuracy. CNN outperforms CNN + LSTM in terms of mean accuracy.

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Network DDOS Attacks Using Convolutional Neural Networks with CNN + LSTM for Improving Accuracy

  • 会议论文

  • 发表日期：

    2025年

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  • D. Saravana Velu,
  • U. Arul

摘要

This Convolutional neural networks and CNN combined with (CNN + LSTM) are used to assess the prediction accuracy of network denial-of-service attacks. These models are used to improve the accuracy of online forecasts using datasets of Network DDOS attacks. Each of the 3100 records in the datasets has 420 rows and 25 columns. MedCalc is used to estimate a sample size of 10 per group with an alpha of 0.05 and a power of 0.8. The performance of the models is used to evaluate how accurate the Network DDOS attack predictions are. The findings show that when it comes to outcome prediction, the convolutional neural network (92.95%) outperforms CNN + LSTM (86.33%). This illustrates the findings’ statistical significance. Compared to convolutional neural networks, CNN + LSTM predicts network DDOS attacks with a lower average accuracy. CNN outperforms CNN + LSTM in terms of mean accuracy.