Detection of malware infection using score level fusion with Kernel Fisher Discriminant Analysis

Masatsugu Ichino, Yusuke Otsuki, Mitsuhiro Hatada, H. Yoshiura
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Abstract

The malware attack is increasing in both breadth and depth, and damage from botnets, whose activities are unabated, and infections from the Web have recently increased. We therefore studied the malware infection detection method by comparing malware traffic with normal traffic. We propose the malware infection detection using score level feature fusion with Kernel Fisher Discriminant Analysis.
基于分数融合和核Fisher判别分析的恶意软件感染检测
恶意软件攻击的广度和深度都在增加,僵尸网络的破坏有增无减,最近来自网络的感染也有所增加。因此,我们通过将恶意软件流量与正常流量进行比较,研究了恶意软件感染检测方法。提出了一种基于分数水平特征融合核费雪判别分析的恶意软件感染检测方法。
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