计算机科学 ›› 2018, Vol. 45 ›› Issue (10): 160-165.doi: 10.11896/j.issn.1002-137X.2018.10.030
王建, 张仰森, 陈若愚, 蒋玉茹, 尤建清
WANG Jian, ZHANG Yang-sen, CHEN Ruo-yu, JIANG Yu-ru, YOU Jian-qing
摘要: 随着互联网络技术的快速发展,各种恶意访问行为危及到网络的信息安全,因此辨识访问用户的角色并识别用户的恶意访问行为对于网络安全具有十分重要的理论意义和实用价值。首先,以网络日志数据为基础,通过建立IP辅助数据库,构建IP用户的日角色模型,在此基础上,引入滑动时间窗技术,将时间的变化动态地融入用户角色辨识,建立了基于滑动时间窗的用户角色动态辨识模型。然后,在分析用户恶意访问流量特征的基础上,将用户访问流量特征和用户信息熵特征进行加权,构建基于多特征的用户恶意访问行为的辨识模型。该模型能够对爆发性和高持续性的恶意访问行为以及少量但大规模分散访问的恶意行为进行识别。最后,采用大数据存储和Spark内存计算技术,对所建立的模型进行实现。实验结果表明,在网络流量产生异常时,所提出的模型能够发现具有恶意访问行为的用户,并准确且高效地辨别出该用户的角色,从而验证了其有效性。
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