融合半字节二阶马尔可夫链与卷积增强Transformer的加密流量分类方法

doi:10.3969/j.issn.1671-1122.2026.07.004

摘要/Abstract

摘要：

针对加密流量分类中特征表达受限、深层时序依赖建模能力不足以及卷积神经网络与Transformer结构融合不充分的问题，文章提出一种融合半字节二阶马尔可夫链与卷积增强Transformer（ConvFormer）的加密流量分类方法NSOM-CF。通过对原始流量字节序列进行半字节级建模，NSOM-CF构建出可反映字节转移规律的二阶马尔可夫转移概率矩阵，可有效缓解矩阵稀疏性，并充分保留捕获流量的动态变化规律和时序依赖关系。进一步映射转移矩阵为多通道特征图，NSOM-CF利用ConvFormer捕获局部空间特征和全局上下文依赖关系，得到特征图向量表示，以此实现加密流量的精准分类。实验结果表明，NSOM-CF在ISCX-VPN和USTC-TFC数据集上分别取得0.9967和0.9968的准确率，验证了该方法在加密流量分类任务中的有效性。

关键词: 加密流量分类, 马尔可夫链, 稀疏性缓解, ConvFormer

Abstract:

To address the limitations in feature representation and insufficient modeling of deep temporal dependencies in encrypted traffic classification, as well as the inadequate integration of convolutional neural networks and Transformer architectures, this paper proposed NSOM-CF, an encrypted traffic classification method that combines a semi-byte second-order Markov chain with a convolution-enhanced Transformer (ConvFormer). By modeling the original traffic byte sequences at the semi-byte level, NSOM-CF constructed a second-order Markov transition probability matrix that reflected byte transition patterns, effectively alleviating matrix sparsity while preserving the dynamic variations and temporal dependencies of traffic. By further mapping the transition matrix into multi-channel feature maps, NSOM-CF leveraged ConvFormer to capture both local spatial features and global contextual dependencies, generating vector representations of the feature maps to achieve accurate encrypted traffic classification. Experimental results show that the proposed method achieves accuracies of 0.9967 and 0.9968 on the ISCX-VPN and USTC-TFC datasets, respectively, demonstrating the effectiveness of NSOM-CF in encrypted traffic classification tasks.

Key words: encrypted traffic classification, Markov chain, sparse reduction, ConvFormer

中图分类号:

TP309

冯景瑜, 罗晨, 杨帅毅, 樊雨. 融合半字节二阶马尔可夫链与卷积增强Transformer的加密流量分类方法[J]. 信息网络安全, 2026, 26(7): 1044-1057.

Feng Jingyu, Luo Chen, Yang Shuaiyi, Fan Yu. A semi-byte second-order Markov chain and convolution-enhanced Transformer-based method for encrypted traffic classification[J]. Netinfo Security, 2026, 26(7): 1044-1057.

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数据集	类型	标签
ISCX-VPN	VPN	VPN_VoIP,VPN_FileTransfer,VPN_Chat, VPN_Email,VPN_Streaming
ISCX-VPN	NonVPN	NonVPN_VoIP, NonVPN_FileTransfer, NonVPN_Chat, NonVPN_Email, NonVPN_Streaming
USTC-TFC	Malware	Cridex, Geodo, Htbot, Miuref, Neris, Nsis-ay, Shifu, Tinba, Virut, Zeus
USTC-TFC	Benign	BitTorrent, Facetime, FTP,Gmail, MySQL, Outlook, Skype, SMB,Weibo,WorldOfWarcraft

数据集	方法	Acc	PR	RC	F1
USTC-TFC	Markov-16	0.9812	0.9715	0.9737	0.9754
	Markov-256	0.9701	0.9516	0.9445	0.9441
	TGI	0.9749	0.9462	0.9449	0.9455
	NSOM-Payload	0.9783	0.9493	0.9484	0.9476
	NSOM	0.9885	0.9773	0.9768	0.9790
ISCX-VPN	Markov-16	0.9747	0.9648	0.9609	0.9628
	Markov-256	0.8684	0.8453	0.8176	0.8312
	TGI	0.7172	0.7016	0.6784	0.6898
	NSOM-Payload	0.7446	0.7269	0.7048	0.7157
	NSOM	0.9861	0.9759	0.9782	0.9770

数据集	方法	Acc	PR	RC	F1
USTC-TFC	AppScanner	0.8954	0.8984	0.8968	0.8892
	GraphDApp	0.8789	0.8226	0.8260	0.8234
	FS-Net	0.8846	0.8846	0.8920	0.8840
	DeepPacket	0.9640	0.9650	0.9631	0.9641
	PERT	0.9909	0.9911	0.9910	0.9911
	ET-BERT	0.9929	0.9930	0.9930	0.9930
	NSOM-CF	0.9968	0.9892	0.9889	0.9890
ISCX-VPN	AppScanner	0.7182	0.7339	0.7225	0.7197
	GraphDApp	0.5977	0.6045	0.6220	0.6036
	FS-Net	0.7205	0.7502	0.7238	0.7137
	DeepPacket	0.9329	0.9377	0.9306	0.9321
	PERT	0.9352	0.9400	0.9349	0.9368
	ET-BERT	0.9890	0.9891	0.9890	0.9890
	NSOM-CF	0.9967	0.9851	0.9847	0.9849

数据集	方法	Down	Acc	PR	RC	F1	参数量/ 计算量
USTC-TFC	Baseline	Strdie	0.9740	0.9406	0.9379	0.9392	3.42M/495.59M
	Markov	Strdie	0.9922	0.9824	0.9817	0.9820	3.43M/123.31M
	Markov+CSCA	Strdie	0.9958	0.9876	0.9861	0.9873	3.45M/125.88M
	Markov+CSCA	Pooling	0.9953	0.9868	0.9845	0.9864	3.13M/109.10M
	Markov+CSCA	IRDonw	0.9968	0.9892	0.9889	0.9890	3.51M/149.38M
ISCX-VPN	Baseline	Strdie	0.9051	0.8872	0.8736	0.8768	3.42M/495.59M
	Markov	Strdie	0.9932	0.9823	0.9814	0.9816	3.43M/123.31M
	Markov+CSCA	Strdie	0.9956	0.9835	0.9829	0.9832	3.45M/125.88M
	Markov+CSCA	Pooling	0.9921	0.9782	0.9775	0.9778	3.13M/109.10M
	Markov+CSCA	IRDonw	0.9967	0.9851	0.9847	0.9849	3.51M/149.38M