计算机科学 ›› 2020, Vol. 47 ›› Issue (1): 287-292.doi: 10.11896/jsjkx.181102118
蒋鹏飞,魏松杰
JIANG Peng-fei,WEI Song-jie
摘要： 针对目前移动应用数目庞大、功能复杂,并且其中混杂着各式各样的恶意应用等问题,面向Android平台分析了应用程序的网络行为,对不同类别的应用程序设计了合理的网络行为触发事件以模拟网络交互行为,提出了网络事件行为序列,并利用改进的深度森林模型对应用进行分类识别,最优分类准确率可达99.03%,并且其具有高精确率、高召回率、高F1-Score和低训练时间的特点。此外,为了解决应用样本数量有限且数据获取时间开销大等难题,还提出了一种使用CWGAN-GP的数据增强方法。与原始生成对抗网络相比,该模型训练更加稳定,仅需一次训练即可生成指定类别的数据。实验结果表明,在加入生成数据共同训练深度森林模型后,其分类准确率提高了9%左右。
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