计算机科学 ›› 2020, Vol. 47 ›› Issue (7): 287-291.doi: 10.11896/jsjkx.190300045
李欣, 段詠程
LI Xin, DUAN Yong-cheng
摘要： 网络安全态势感知作为网络安全防护措施的有效补充,是近年来的研究热点之一,而准确地评估网络安全状态已成为网络安全领域的一个重要课题。隐马尔可夫模型(Hidden Markov Model,HMM)可用于网络安全态势评估,能实时评估网络状态,但其存在模型参数难以配置、评估准确率较低等问题。因此,文中提出了一种改进隐马尔可夫模型的态势评估方法,将模型Baum-Welch(BW)参数优化算法与人群搜索算法(Seeker Optimization Algorithm,SOA)相结合,利用SOA随机搜索能力强的特点,解决传统参数优化算法容易陷入局部最优解的问题,将优化后的参数代入HMM中,通过量化分析得出网络安全态势值。基于DARPA2000数据集采用MATLAB软件对提出的方法进行实验验证,结果表明,与BW算法相比,所提方法能够提高模型准确率,对网络安全态势的量化更加合理。
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