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In a Distributed Denial of Service (DDoS) attack, a network of compromised devices is used to overwhelm a target with a flood of requests, making it unable to serve legitimate requests. The detection of these attacks is a challenging issue in cybersecurity, which has been addressed using Machine Learning (ML) and Deep Learning (DL) algorithms. Although ML/DL can improve the detection accuracy, but they can still be evaded - ironically - through the use of ML/DL techniques in the generation of the attack traffic. In particular, Generative Adversarial Networks (GAN) have proven their efficiency in mimicking legitimate data. We address the above aspects of ML/DL-based DDoS detection and anti-detection techniques. First, we propose a DDoS detection method based on the Long Short-Term Memory (LSTM) model, which is a type of Recurrent Neural Networks (RNNs) capable of learning long-term dependencies …