基于无监督非负矩阵分解的TAP规则推荐

doi:10.3969/j.issn.1671-1122.2023.02.011

信息网络安全 ›› 2023, Vol. 23 ›› Issue (2): 96-103.doi: 10.3969/j.issn.1671-1122.2023.02.011

基于无监督非负矩阵分解的TAP规则推荐

王明, 邢永恒, 王峰()

吉林大学计算机科学与技术学院，长春 130012

收稿日期:2022-11-28 出版日期:2023-02-10 发布日期:2023-02-28
通讯作者: 王峰 E-mail:wangfeng12@mails.jlu.edu.cn
作者简介:王明（1998—），男，山西，硕士研究生，主要研究方向为物联网数据挖掘|邢永恒（1996—），男，吉林，博士研究生，主要研究方向为物联网数据挖掘和隐私保护|王峰（1987—），男，吉林，副教授，博士，主要研究方向为计算机系统架构和网络空间安全
基金资助:
国家重点研发计划(2017YFA0604500);吉林省科技发展计划(20220101115JC);吉林省发改委医疗大数据安全处理平台项目(2019FGWTZC001)

Unsupervised Matrix Factorization Based Trigger Action Programming Rules Recommendation

WANG Ming, XING Yongheng, WANG Feng()

College of Computer Science and Technology, Jilin University, Changchun 130012, China

Received:2022-11-28 Online:2023-02-10 Published:2023-02-28
Contact: WANG Feng E-mail:wangfeng12@mails.jlu.edu.cn

摘要/Abstract

摘要：

TAP在定制物联网设备联动过程中得到了广泛的应用。TAP数据中除了包含物品之间的条件触发关系外，还包含用户对于相关规则的文本描述信息。如何使用TAP数据的多源异构属性进行数据处理是物联网应用中重要的研究之一。文章将TAP数据建模成含有多种节点和边类型的异质图，实现了多源异构数据之间多类型关系的融合处理，进而根据不同类型节点之间的连接关系生成关系矩阵。文章通过非负矩阵分解（NMF）以无监督方式学习TAP异质图中每个节点的特征向量用于TAP规则推荐。文章提出3种带权的关系矩阵生成方法，分别为共现频率权值（CFW）、概念相似度权值（CSW）和TF-IDF权值（TIW）。实验结果表明，在由CFW生成的矩阵上进行NMF，由此获得的特征向量在TAP规则推荐时表现良好。

关键词: 触发执行编程, 非负矩阵, 权值矩阵

Abstract:

TAP has been widely used in customized IoT device linkage. In addition to the conditional trigger relationship between items, the TAP data also contains the text description information about the relevant rules. How to use the multi-source heterogeneous attributes of TAP data is one of the important researches in the application of the IoT. In this paper, TAP data was modeled as a heterogeneous graph containing multiple types of nodes and edges, which realized the fusion of multiple types of relationships between multi-source heterogeneous data, and then generated a relationship matrix according to the connection between different types of nodes. Non-negative matrix factorization (NMF) was used to unsupervised learn the feature of each node in the TAP heterogeneous graph for TAP rule recommendation. This paper proposed three weighted relational matrix generation methods, which were called co-occurrence frequency weight (CFW), concept similarity weight (CSW) and TF-IDF weight (TIW). The experimental results show that feature vectors obtained from NMF, which decomposes the matrix generated by CFW have better performance in TAP rule recommendation.

Key words: trigger-action programming, non-negative matrix, weighted matrix

中图分类号:

TP309

王明, 邢永恒, 王峰. 基于无监督非负矩阵分解的TAP规则推荐[J]. 信息网络安全, 2023, 23(2): 96-103.

WANG Ming, XING Yongheng, WANG Feng. Unsupervised Matrix Factorization Based Trigger Action Programming Rules Recommendation[J]. Netinfo Security, 2023, 23(2): 96-103.

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权重矩阵	Precision@1	Precision@3	Precision@5
SR_128	0.6474	0.5507	0.4955
CFW_128	0.6783	0.5697	0.5100
CSW_128	0.6463	0.5417	0.4851
TIW_128	0.3066	0.2630	0.2399
SR_256	0.6673	0.5451	0.4809
CFW_256	0.6853	0.5837	0.5134
CSW_256	0.6763	0.5607	0.5042
TIW_256	0.3256	0.2700	0.2417
SR_512	0.6643	0.5514	0.4899
CFW_512	0.6913	0.5907	0.5248
CSW_512	0.6713	0.5594	0.4931
TIW_512	0.3896	0.3276	0.2923

权重矩阵	MRR
SR_128	0.8380
CFW_128	0.8559
CSW_128	0.8502
TIW_128	0.6217
SR_256	0.8557
CFW_256	0.8603
CSW_256	0.8465
TIW_256	0.6454
SR_512	0.8491
CFW_512	0.8708
CSW_512	0.8539
TIW_512	0.6714

基于无监督非负矩阵分解的TAP规则推荐

Unsupervised Matrix Factorization Based Trigger Action Programming Rules Recommendation

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