本发明公开了一种基于深度强化学习Double Deep Q‑Network的半监督式Double Deep Q‑Network(semi‑supervised Double Deep Q‑Network，SSDDQN)的网络异常流量检测方法，涉及计算机网络安全技术领域。方法包括：获取计算机网络流量数据的训练样本，建立神经网络，采用SSDDQN的方式利用训练样本对神经网络进行训练，对神经网络的参数进行更新，最后利用训练好的神经网络对网络流量数据进行异常检测。本发明SSDDQN的方式，不但可以降低人工标注成本，提高学习性能，而且神经网络更简单、快速，更易部署在苛刻的网络环境当中，同时也提升了未知攻击的检测准确率。
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