基于自适应交互反馈的电力终端信任度评估机制

doi:10.11772/j.issn.1001-9081.2022050717

《计算机应用》唯一官方网站 ›› 2023, Vol. 43 ›› Issue (6): 1878-1883.DOI: 10.11772/j.issn.1001-9081.2022050717

基于自适应交互反馈的电力终端信任度评估机制

魏兴慎¹(), 高鹏¹, 吕卓², 曹永健¹, 周剑¹, 屈志昊³

^1.南瑞集团有限公司(国网电力科学研究院有限公司) 南京南瑞信息通信科技有限公司, 南京 201137
^2.国网河南省电力公司电力科学研究院, 郑州 450052
^3.河海大学计算机与信息学院, 南京 211100

收稿日期:2022-05-23 修回日期:2022-12-19 接受日期:2023-01-10 发布日期:2023-06-08 出版日期:2023-06-10
通讯作者: 魏兴慎
作者简介:魏兴慎（1986—），男，宁夏固原人，高级工程师，硕士，CCF会员，主要研究方向：网络安全、图神经网络Email：weixingshen@sgepri.sgcc.com.cn
高鹏（1982—），男，江苏连云港人，高级工程师，博士，CCF会员，主要研究方向：网络安全、网络信息抽取
吕卓（1986—），男，山西忻州人，高级工程师，硕士，主要研究方向：信息安全
曹永健（1990—），男，江苏南通人，工程师，硕士，主要研究方向：信息安全
周剑（1988—），男，江苏扬州人，工程师，主要研究方向：网络安全
屈志昊（1989—），男，山东东明人，副教授，博士，CCF会员，主要研究方向：边缘计算、物联网、联邦学习。
基金资助:
国家电网有限公司总部科技项目(5108-202224046A-1-1-ZN)

Adaptive interaction feedback based trust evaluation mechanism for power terminals

Xingshen WEI¹(), Peng GAO¹, Zhuo LYU², Yongjian CAO¹, Jian ZHOU¹, Zhihao QU³

^1.Nanjing NARI Information and Communication Technology Company Limited，NARI Group Corporation/State Grid Electric Power Research Institute，Nanjing Jiangsu 201137，China
^2.State Grid Henan Electric Power Research Institute，Zhengzhou Henan 450052，China
^3.School of Computer and Information，Hohai University，Nanjing Jiangsu 211100，China

Received:2022-05-23 Revised:2022-12-19 Accepted:2023-01-10 Online:2023-06-08 Published:2023-06-10
Contact: Xingshen WEI
About author:GAO Peng， born in 1982， Ph. D.， senior engineer. His research interests include network security， network information extraction.
LYU Zhuo， born in 1986， M. S.， senior engineer. His research interests include information security.
CAO Yongjian， born in 1990， M. S.， engineer. His research interests include information security.
ZHOU Jian， born in 1988， engineer. His research interests include network security.
QU Zhihao， born in 1989， Ph. D.， associate professor. His research interests include edge computing， internet of things， federated learning.
Supported by:
Science and Technology Project of State Grid Corporation of China(5108-202224046A-1-1-ZN)

摘要/Abstract

摘要：

在电力系统中，终端设备的信任度评估是实现访问权限分级、数据安全采集的关键技术，对于保证电网安全稳定运行具有重要意义。传统的信任度评估模型通常基于终端设备身份识别、运行状态和交互记录等直接计算信任度评分，在面临间接攻击和节点共谋时，性能较差。针对上述问题，提出一种基于自适应交互反馈的信任度评估（Adaptive Interaction Feedback based Trust evaluation， AIFTrust）机制。所提机制通过直接信任评估模块、信任推荐模块和信任聚合模块全面地度量设备的信任等级，针对电力信息系统中海量协作终端精准地评估信任度。首先，直接信任评估模块引入交互成本，并基于信任衰减策略计算恶意目标终端的直接信任评分；其次，信任推荐模块引入经验相似性，并通过二次聚类推荐相似终端以提高推荐信任度评分的可靠性；然后，信任聚合模块基于信任评分准确性自适应地聚合直接信任度评分和推荐信任度评分。在真实数据集和生成数据集上的仿真实验结果均表明，在攻击概率为30%、信任衰减率为0.05时，AIFTrust相较于基于相似度的信任评估方法SFM（Similarity FraMework）和基于客观信息熵的信任评估方法CRT（Reputation Trusted based on Cooperation）在推荐准确度上分别提高13.30%和14.81%。

关键词: 信任度评估, 交互成本, 经验相似性, 信任聚合, 信任衰减

Abstract:

In power system， the trust evaluation of terminals is a key technology to grade the access and securely collect data， which is critical to ensure the safe and stable operation of the power grid. Traditional trust evaluation models usually calculate the trust score directly based on identification， running states and interaction histories， etc. of the terminals， and show poor performance with indirect attacks and node collusion. To address these problems， an Adaptive Interaction Feedback based Trust evaluation （AIFTrust） mechanism was proposed. In the proposed mechanism， device trust level was measured comprehensively based on direct trust evaluation module， trust recommendation module and trust aggregation module， and accurate trust evaluation for massive collaborative terminals in power information systems was achieved. First， the interaction cost was introduced by the direct trust evaluation module， and the direct trust score of the malicious target terminal was calculated on the basis of the trust decay policy. Then， the experience similarity was introduced by the trust recommendation evaluation module， and similar terminals were recommended through secondary clustering to improve the reliability of the recommendation trust scoring. After the above， the trust aggregation module was used to adaptively aggregate the direct trust score and the recommendation trust score based on the trust score accuracy. Simulation results on real datasets and synthetic datasets show that when attack probability is 30% and trust decay rate is 0.05， AIFTrust improves the recommendation accuracy by 13.30% and 14.81% compared to the similarity-based trust evaluation method SFM （Similarity FraMework） and the trust evaluation method based on objective information entropy CRT （Reputation Trusted based on Cooperation）， respectively.

Key words: trust evaluation, interaction cost, experience similarity, trust aggregation, trust decay

中图分类号:

TP399

魏兴慎, 高鹏, 吕卓, 曹永健, 周剑, 屈志昊. 基于自适应交互反馈的电力终端信任度评估机制[J]. 计算机应用, 2023, 43(6): 1878-1883.

Xingshen WEI, Peng GAO, Zhuo LYU, Yongjian CAO, Jian ZHOU, Zhihao QU. Adaptive interaction feedback based trust evaluation mechanism for power terminals[J]. Journal of Computer Applications, 2023, 43(6): 1878-1883.

图/表 6

图1 AIFTrust的整体设计

Fig. 1 Overview design of AIFTrust

图2 推荐信任评分方法

Fig. 2 Recommendation trust scoring method

图3 直接信任分数

Fig. 3 Direct trust score

表1 AIFTrust相较于不同算法的准确率提升比例 (%)

Tab.1 Accuracy improvement of AIFTrust compared with different algorithms

攻击概率/%	AIFTrust相较于SFM算法			AIFTrust相较于CRT算法
攻击概率/%	λ=0.05	λ=0.1	λ=0.15	λ=0.05	λ=0.1	λ=0.15
10	4.55	2.09	0.45	2.92	1.74	1.08
20	6.85	3.56	2.39	8.85	6.70	4.06
30	13.30	10.75	12.31	14.81	12.63	12.31

表2 IoT-23数据集上不同算法的推荐准确率对比 (%)

Tab.2 Recommendation accuracy comparison of different algorithms on IoT-23 dataset

攻击概率	H-Reputation	SFM	CRT	AIFTrust
10	81.50	86.70	86.70	88.90
20	72.80	78.20	77.20	83.20
30	64.60	73.50	69.00	77.40

表3 不同时间片下不同算法运行时间对比 (ms)

Tab.3 Running time comparison of different algorithms in different time slices

算法	不同时间片时的运行时间
算法	125	250	500	1 000
H-Reputation	3.12	14.28	60.08	388.75
CRT	3.37	15.52	80.21	490.18
SFM	4.71	18.29	100.35	678.12
AIFTrust	3.33	15.11	73.12	420.18

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基于自适应交互反馈的电力终端信任度评估机制

Adaptive interaction feedback based trust evaluation mechanism for power terminals

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