计算机科学 ›› 2022, Vol. 49 ›› Issue (6A): 508-515.doi: 10.11896/jsjkx.210700103
姚烨, 朱怡安, 钱亮, 贾耀, 张黎翔, 刘瑞亮
YAO Ye, ZHU Yi-an, QIAN Liang, JIA Yao, ZHANG Li-xiang, LIU Rui-liang
摘要： 针对单一分类模型检测精度有限的问题,提出了一种基于异质模型融合的Android恶意软件检测方法。首先识别和采集恶意软件混合特征信息,采用基于CART决策树的随机森林算法和基于MLP的Adaboost算法分别构造集成学习模型,然后通过Blending算法对这两个分类器进行模型融合,最后得到一种异质模型融合分类器,在此基础上实施移动终端恶意软件检测。实验结果表明所提方法能够有效克服单一分类模型检测精度不足的问题。
中图分类号:
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