Distributed Denial of Service Attacks in Cloud Computing based on Deep Learning: A Study
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Abstract
Cloud computing has transformed the digital landscape by offering scalable and cost-effective resources. However, its dynamic and shared infrastructure makes it a prime target for cyberattacks, particularly Distributed Denial of Service (DDoS) attacks. These attacks disrupt the availability of services by overwhelming cloud systems with illegitimate traffic from multiple distributed sources. Traditional detection methods often fall short in identifying sophisticated and large-scale DDoS patterns in real time. This study explores the application of deep learning (DL) techniques for detecting and mitigating DDoS attacks in cloud environments. By leveraging models such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and autoencoders, the research aims to accurately classify malicious traffic patterns and distinguish them from legitimate behavior. A deep learning-based Intrusion Detection System (IDS) is proposed, trained on real-world datasets such as CICDDoS2019 and UNSW-NB15. The experimental results demonstrate that DL models significantly outperform traditional machine learning approaches in terms of accuracy, adaptability, and real-time performance. This study contributes to the advancement of intelligent cloud security systems and highlights the potential of deep learning in building resilient and proactive defense mechanisms against evolving cyber threats in cloud computing.
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