Polymorphic Network Control and Security Monitor Based on P4

doi:10.3969/j.issn.1671-1122.2026.01.010

Abstract

Abstract:

Programmable network technology controls network devices and data packets through software-defined and programming techniques, enhancing network flexibility, scalability, and automation capabilities, thereby laying a solid foundation for the development of multimodal networks. Based on a programmable architecture, this paper designed a data packet routing and forwarding mechanism for six modalities: identity, content, geographical location, elastic address space, IPv4, and IPv6, and implemented packet parsing, routing lookup, and forwarding at the data plane. Simultaneously, a multimodal network control system was constructed to support functions such as packet parsing, topology management, flow table generation and distribution, and network measurement. It integrated resource coordination and scheduling algorithms to analyze network status in real time, compute routing rules, and distribute flow tables. Through traffic feature extraction, this paper achieves security detection and builds a multimodal network traffic time-series model based on deep learning to realize anomaly detection and identification, introducing intrinsic security features to ensure system availability and reliability. Experimental results demonstrate that the proposed scheme enables unified communication and control of multimodal networks, supporting multiple modalities. The control system is functionally complete and performs stably, with a topology scale exceeding 2000 nodes and end-to-end latency below 100ms. The security detection function can identify abnormal traffic and network modalities in real time, with an anomaly detection accuracy rate of 96.49% and a modality recognition accuracy rate of 99.72%.

Key words: polymorphic network, software defined network, network measurement, anomaly detection

CLC Number:

TP309

LI Dong, GAO Yuan, YU Junqing, ZENG Muhong, CHEN Junxin. Polymorphic Network Control and Security Monitor Based on P4[J]. Netinfo Security, 2026, 26(1): 115-124.

Figures/Tables 11

References 35

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符号及名称	说明
流量集合P	多模态网络中所有模态数据包的集合
拓扑视图g	控制平面获取的拓扑整体视图
路由策略$\pi $	流表模块根据模态标识计算的最佳路径
消息队列Q	生产者C ，消费者F
网络状态S	进行多模态安全测量的网络状态
测量指标V	经过网络测量得到的网络指标
解析模块I	输入数据包$p\in P$，输出模态标识$m$
控制模块C	$\left\{ g,\pi,Q \right\}$
流表模块F	输入模态标识$m$，输出路由策略$\pi $和流表项$\Phi $
测量模块T	输入网络状态$s\in S$，输出测量指标$\mathcal{V}$
安全模块S	输入$\mathcal{V}$，输出异常检测结果$\tau $

算法	精确率	召回率	F1分数
Transformer	96.49%	94.68%	95.58%
LSTM	95.96%	93.64%	94.79%
TCN	95.96%	93.67%	94.80%
RNN	74.39%	65.62%	69.67%

算法	精确率	召回率	F1分数
Transformer	99.72%	99.71%	99.71%
LSTM	99.60%	99.59%	99.59%
TCN	99.63%	99.63%	99.63%
RNN	99.21%	99.20%	99.20%