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The distributed denial of service (DDoS) attack is one of the most server threats to the current Internet and brings huge losses to society. Furthermore, it is challenging to defend DDoS due to the case that the DDoS traffic can appear similar to the legitimate ones. Router throttling is an accessible approach to defend DDoS attacks. Some existing router throttling methods dynamically adjust a given threshold value to keep the server load safe. However, these methods are not ideal as they exploit the information of the current time, so the perception of time series variations is poor. The DDoS problem can be seen as a Markov decision process (MDP). Multi-agent router throttling (MART) method based on hierarchical communication mechanism has been proposed to address this problem. However, each agent is independent with each other and has no communication among them, therefore, it is hard for them to collaborate to learn an ideal policy to defend DDoS. To solve this multi-agent partially observable MDP problem, we propose a centralized reinforcement learning router throttling method based on a centralized communication mechanism. Each router sends its own traffic reading to a central router, the central router then makes a decision for each router to choose the throttling rate. We also simulate the environment of the DDoS problem more realistic while modify the reward function of the MART to make the reward function of more coherent. To decrease the communication costs, we add a deep deterministic policy gradient network for each router to decide whether or not to send information to the central agent. The experiments validate that our proposed new smart router throttling method outperforms existing methods to the DDoS instruction response.INDEX TERMS Distributed denial of service, router throttling, Markov decision process, multi-agent router throttling, hierarchical communication, centralized communication, communication costs.

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The explosive growth of malware variants poses a continuously and deeply evolving challenge to information security. Traditional malware detection methods require a lot of manpower. However, machine learning has played an important role on malware classification and detection, and it is easily spoofed by malware disguising to be benign software by employing self-protection techniques, which leads to poor performance for existing techniques based on the machine learning method. In this paper, we analyze the local maliciousness about malware and implement an anti-interference detection framework based on API fragments, which uses the LSTM model to classify API fragments and employs ensemble learning to determine the final result of the entire API sequence. We present our experimental results on Ali-Tianchi contest API databases. By comparing with the experiments of some common methods, it is proved that our method based on local maliciousness has better performance, which is a higher accuracy rate of 0.9734.