基于Attention-DenseNet-BC的恶意软件家族分类方法

Method of Malware Family Classification Based on Attention-DenseNet-BC Model Mechanism

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。

Malware is one of the most serious threats to the Internet.The existing malware has huge data size and various features.Convolutional Neural Network has the features of autonomous learning,which can be used to solve the problems that the feature extraction of malware is complex and the feature selection is difficult.However,in convolutional neural network,conti-nuously increasing the network layers will cause a disappear of the gradient,leading to a degradation of network performance and low accuracy.To solve this problem,an Attention-DenseNet-BC model that is suitable for malware image detection is proposed.First,the Attention-DenseNet-BC model is constructed by combining the DenseNet-BC network and the attention mechanism.Then,the malware images are used as the input of the model,and the detection results are obtained by training and testing the model.The experimental results indicate that compared with other deep learning models,the Attention-DenseNet-BC model can achieve better classification results.A high classification accuracy can be attained with the model based on the malimg public dataset.