Abstract
DeepDefend is an advanced framework for real-time detection and prevention of DDoS attacks in cloud environments. It employs deep learning techniques, notably CNN-LSTM-Transformer networks, to predict network traffic entropy and detect potential attacks. The framework uses a genetic algorithm for optimal feature selection, enhancing the efficacy of the AutoCNN-DT model in distinguishing between normal and attack traffic. Tested on the CIDDS-001 traffic dataset, DeepDefend demonstrates high accuracy in entropy forecasting and rapid, precise detection of DDoS attacks. This integrated approach combines time series analysis, genetic algorithms, and deep learning, offering a robust solution to protect cloud computing infrastructure against DDoS threats.
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More From: Journal of King Saud University - Computer and Information Sciences
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