The provided text centers on the critical issue of Distributed Denial-of-Service (DDoS) attacks and explores advanced methods for their detection and mitigation. The main source presents a novel hybrid model that combines a 1D Convolutional Neural Network for feature extraction with Random Forest and Multi-layer Perceptron classifiers for accurate identification of diverse DDoS attacks, achieving promising results on the CIC-DDoS2019 dataset. Furthermore, it discusses the integration of this model with Snort, an intrusion detection and prevention system, to create a more robust and adaptive security solution. Additional cited works offer context by examining existing research, its limitations concerning evolving threats and datasets, and alternative machine learning approaches to tackle DDoS attacks in various network environments, including IoT and cloud computing, while also highlighting the importance of real-world testing.
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