摘要
随着加密技术的全面应用,越来越多的恶意软件同样采用加密的方式隐藏自身的网络活动,导致基于规则和特征的传统方法无法满足准确性和普适性的要求.针对上述问题,提出一种层次特征融合和注意力的恶意加密流量识别方法.算法具备层次结构,依次提取数据包的特征和会话流的特征,前一阶段设计全局混合池化方法进行特征融合;后一阶段使用注意力机制提高BiLSTM网络分析序列关系的能力.最终,实验采用CIC-AndMal 2017数据集进行验证,结果表明:模型设计合理,相比TextCNN模型和HST-MHSA模型,漏报率分别降低5.8%和2.6%,加权F1值分别提高4.7%和3.5%,在恶意加密流量识别和分类方面体现良好的优化效果.
With the comprehensive application of encryption techniques,a growing number of malware also resort to encryption to hide their activities online,consequently preventing traditional methods based on patterns and features from meeting the requirements of accuracy and universality.To solve this problem,this study proposes a malicious encrypted traffic identification method based on hierarchical feature fusion and attention.The algorithm has a hierarchical structure and sequentially extracts the features of data packets and session flows.In the former phase,a global mixed pooling method is designed for feature fusion.In the latter phase,the attention mechanism is used to improve the ability of the bidirectional long short-term memory(BiLSTM)network to analyze sequential relationships.Finally,verification experiments are conducted on the CIC-AndMal 2017 dataset,and the results show that the proposed model is welldesigned.Compared with the text convolutional neural network(TextCNN)model and the hierarchical spatiotemporal feature and multi-head self-attention(HST-MHSA)model,the proposed model reduces the false negative rate respectively by 5.8%and 2.6%and increases the weighted F1-score respectively by 4.7%and 3.5%.In other words,the proposed model achieves a satisfactory optimization effect in the identification and classification of malicious encrypted traffic.
作者
包文博
沙乐天
曹晓梅
BAO Wen-Bo;SHA Le-Tian;CAO Xiao-Mei(School of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing 210023,China)
出处
《计算机系统应用》
2023年第1期358-367,共10页
Computer Systems & Applications
关键词
异常流量检测
加密流量识别
深度学习
特征融合
注意力机制
abnormal traffic detection
encrypted traffic identification
deep learning
feature fusion
attention mechanism
