网络空间作战中利用强化学习方法防御放大式拒绝服务攻击

Using Reinforcement Learning Method to Defense Amplification DDoS Attack in Cyberspace Operations

为解决放大式拒绝服务攻击给赛博安全带来的风险,提出一种基于强化学习的方法。以DNS的放大攻击为对象,构建一个简化的放大攻击模型,利用model-free方法获取不同状态间的转移概率,采用强化学习方法建立防御放大攻击模型,通过对放大攻击模式的学习制定流量抑制策略,并对其进行仿真实验验证。结果表明:该方法能够有效挖掘出放大攻击的流量模式,智能化抵御来自放大攻击的威胁。

To resist the risk of amplification DDoS attack,which is likely to cause significant damage to cyber security,a reinforcement learning method is proposed.Taking DNS as the target of attack,a simplified amplification attack model is constructed.The transition probability between different states is obtained by using the model-free method.Then,the reinforcement learning method is used to build up to defense the attack,and the traffic suppression strategy is formulated by learning the amplification attack mode.Finally,the simulation results show that the proposed reinforcement learning method can effectively dig out the traffic pattern of amplification DDoS attack and intelligently resist the threat.