计算机科学 ›› 2021, Vol. 48 ›› Issue (6): 338-342.doi: 10.11896/jsjkx.201200239
王金恒1, 单志龙2, 谭汉松3, 王煜林1
WANG Jin-heng1, SHAN Zhi-long2, TAN Han-song3, WANG Yu-lin1
摘要： 为提高网络安全态势评估的准确率,提出了一种基于遗传优化概率神经网络的网络安全态势评估。首先,在网络安全态势评估建模过程中,根据网络安全态势特点和常见评估等级建立了概率神经网络的网络安全态势评估模型,以便充分挖掘概率神经网络在网络安全态势评估细粒度方面的优势。然后,为了防止因网络安全态势参数细粒度评估而造成收敛速度过慢的情况发生,对概率神经网络的修正因子进行遗传算法优化,并再次进行概率神经网络训练,从而得到稳定的概率网络安全态势评估模型。经过实验证明,相比传统的概率神经网络算法,基于遗传算法优化概率神经网络的网络安全态势评估准确度更高,平均准确率达到90%以上,且训练速度更快。
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