计算机科学 ›› 2019, Vol. 46 ›› Issue (11A): 433-437.
王婷婷, 朱江
WGAN Ting-ting, ZHU Jiang
摘要： 文中提出了一种基于差分WGAN(Wasserstein-GAN)的网络安全态势预测机制,该机制利用生成对抗网络(Generative Adversarial Network,GAN)来模拟态势的发展过程,从时间维度实现态势预测。为了解决GAN具有的网络难以训练、collapse mode及梯度不稳定的问题,提出了利用Wasserstein距离作为GAN的损失函数,并采用在损失函数中添加差分项的方法来提高态势值的分类精度,同时还证明了差分WGAN网络的稳定度。实验结果与分析表明,该机制相比其他机制而言,在收敛性、预测精度和复杂度方面具有优势。
中图分类号:
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