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With the rapid development of Internet technology, cybersecurity threats such as security loopholes, data leaks, network fraud, and ransomware have become increasingly prominent, and organized and purposeful cyberattacks have increased, posing more challenges to cybersecurity protection. Therefore, reliable network risk assessment methods and effective network security protection schemes are urgently needed.

Based on the dynamic behavior patterns of attackers and defenders, a Bayesian network attack graph is constructed, and a multitarget risk dynamic assessment model is proposed based on network availability, network utilization impact and vulnerability attack possibility. Then, the self-organizing multiobjective evolutionary algorithm based on grey wolf optimization is proposed. And the authors use this algorithm to solve the multiobjective risk assessment model, and a variety of different attack strategies are obtained.

The experimental results demonstrate that the method yields 29 distinct attack strategies, and then attacker's preferences can be obtained according to these attack strategies. Furthermore, the method efficiently addresses the security assessment problem involving multiple decision variables, thereby providing constructive guidance for the construction of security network, security reinforcement and active defense.

A method for network risk assessment methods is given. And this study proposed a multiobjective risk dynamic assessment model based on network availability, network utilization impact and the possibility of vulnerability attacks. The example demonstrates the effectiveness of the method in addressing network security risks.

Trade credit is an important business-to-business marketing tool for building firms’ competitive advantage. Many studies explore the determinants of trade credits from a trust-based view, but the role of political connections is largely overlooked, despite their potential influence in assessing firms’ trustworthiness in the context of emerging economies. This study aims to fill this gap by examining how political connections affect the capacity of emerging economy firms (EEFs) to grant and receive trade credit.

This study tests a conceptual model using secondary data collected from 1,149 Chinese privately owned listed manufacturing firms between 2008 and 2016.

This study finds that political connections reduce EEFs’ accounts receivable and payable; their philanthropic activities alleviate this negative effect for accounts payable, while patent applications reduce it for accounts receivable. These findings suggest the effect of political connections can spillover to EEFs’ relationship with their up- and down-stream partners.

This study has implications EEF managers, particularly in pointing to the detrimental effect of political connections on relationships with buyers and suppliers, and highlights the need to adopt suitable approaches to offset this effect.

This study sheds new light on the negative effect of political connections on EEFs’ capacity to grant and receive trade credit in their exchanges with up-stream and down-stream partners. It enriches the trust-based view of trade credit by revealing the significant influence of EEFs’ political connections, while also advancing a contingency view by testing the moderating role of corporate philanthropic activities and patent applications.