The router throttling mechanism provides us a chance to prevent DDoS attack proactively through rate-limiting suspicious traffic before effective detection mechanism. The existing search-based and learning-based studies are highly customized to server load and can hardly cope with the constantly changing server load and unseen scenarios. To address the problem above, we design a self-evolutionary DDoS defense system, DeepThrottle, based on deep reinforcement learning (DRL) and router throttling mechanism in software defined network (SDN). The experimental results demonstrate that the DeepThrottle improves the passing ratio of normal traffic to the victim server, and reduces the server load under unseen attack scenarios compared with the state-of-the-art RL-based method.

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