计算机科学 ›› 2021, Vol. 48 ›› Issue (10): 308-314.doi: 10.11896/jsjkx.210200166
李一萌, 李成海, 宋亚飞, 王坚
LI Yi-meng, LI Cheng-hai, SONG Ya-fei, WANG Jian
摘要: 恶意软件是互联网最严重的威胁之一。现存的恶意软件数据庞大,特征多样。卷积神经网络具有自主学习的特点,可以用来解决恶意软件特征提取复杂、特征选择困难的问题。但卷积神经网络连续增加网络层数会引起梯度消失,导致网络性能退化、分类准确率较低。针对此问题,提出了一种适用于恶意软件图像检测的Attention-DenseNet-BC模型。首先结合DenseNet-BC网络和注意力机制(attention mechanism)构建了Attention-DenseNet-BC模型,然后将恶意软件图像作为模型的输入,通过对模型进行训练和测试得到检测结果。实验结果表明,相比其他深度学习模型,Attention-DenseNet-BC模型可以取得更好的分类结果。在Malimg公开数据集上该模型取得了较高的分类精确率。
中图分类号:
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