Abstract

With the development of society and information technology, people’s dependence on the Internet has gradually increased, including online shopping, downloading files, reading books, and online banking. However, how to ensure the safety and legitimacy of these network user behaviors has become the focus of attention. As we all know, cybersecurity and system resilience originate from symmetry. Due to the diversity and unpredictability of cyber-attacks, absolute cybersecurity is difficult to achieve; system resilience indicates that protecting system security should shift from resisting attacks to ensuring system continuity. The trust evaluation of network users is a research hotspot in improving network system security. Aiming at the defects of incomplete evaluation processes and inaccurate evaluation results in current online user behavior trust evaluation methods, this paper combines the basic principles of online user trust evaluation and proposes a trust evaluation model that combines fuzzy Petri nets with user behavior analysis. First, for “unfamiliar” users, we used fuzzy Petri nets to calculate the user’s recommended trust value as the system’s indirect trust value; next, we used the user’s behavior record as evidence to conduct direct trust evaluation on the user to obtain the system’s direct trust in the user’s value; finally, the two calculation results were combined to obtain the user’s comprehensive trust value. In terms of experimental verification, the experimental data came from a self-developed e-book management system. Through theoretical analysis and simulation results, it was shown that the model met the optimization conditions of subjective and objective relative balance, the evaluation process was more complete, and the trust evaluation values of network users could be obtained more accurately. This evaluation method provides solid theory and research ideas for user credibility judgment of key network basic application platforms such as online shopping malls, online transactions, and online banking.

Highlights

  • In recent years, people have used the Internet to share information and participate in various network activities such as downloading documents, shopping online, watching videos, playing games, etc

  • Liu et al [14], Symmetry 2021, 13, 1487 in order to let cloud users find cloud services which satisfied performance preferences, used the comprehensive trust cloud center of gravity assessment method (CCGE) to calculate the trust level of cloud services, introduce the membership theory into the trust evaluation model, and establish a precise trust relationship between cloud users and cloud services based on user performance requirements

  • In order to solve the above problems, we propose a new trust evaluation method that combines the factors of direct trust and indirect trust

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Summary

Introduction

People have used the Internet to share information and participate in various network activities such as downloading documents, shopping online, watching videos, playing games, etc. These all depend on the mutual trust of each participant in the network environment. User verification only relies on traditional identity authentication, which is obviously a defect because a user who accesses the network can enter or leave the network. Information is used to steal confidential data within the company; even personnel within the company use the convenience of their own positions to steal confidential data The occurrence of these two behaviors can evade the checkpoint of identity authentication and cause the loss of network system resources [2]. There is an urgent need to find an effective method that can protect network security and restrain the behavior of malicious users to ensure the security of data resources in the network system [3,4]

Related Work on Web User Trust Evaluation
The Basic Principle
The Basic Definitions
The Framework of Trust Evaluation
Indirect
Indirect Trust Value Based on a Fuzzy Petri Net
Obtaining Evidence of User Behavior
Standardized Processing of User Behavior Evidence
Weight of User Behavior Evidence
Objective Weight
Subjective Weight
Integration Weight
Direct Trust Value Based on User Behavior
Comprehensive Trust Evaluation
Experimental Analysis
(0.85~1)Evaluation ResultHighly trusted user
Conclusions
Full Text
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