A Review on Risk Management in Information Systems: Risk Policy, Control and Fraud Detection

A Fusion Decision-Making Architecture for COVID-19 Crisis Analysis and Management

The COVID-19 outbreak has had considerably harsh impacts on the global economy, such as shutting down and paralyzing industrial production capacity and increasing the unemployment rate. For enterprises, relying on past experiences and strategies to respond to such an unforeseen financial crisis is not appropriate or sufficient. Thus, there is an urgent requirement to reexamine and revise an enterprise’s inherent crisis management architecture so as to help it recover sooner after having encountered extremely negative economic effects. To fulfill this need, the present paper introduces a fusion architecture that integrates artificial intelligence and multiple criteria decision making to exploit essential risk factors and identify the intertwined relations between dimensions/criteria for managers to prioritize improvement plans and deploy resources to key areas without any waste. The result indicated the accurate improvement priorities, which ran in the order of financial sustainability (A), customer and stakeholders (B), enablers’ learning and growth (D), and internal business process (C) based on the measurement of the impact. The method herein will help to effectively and efficiently support crisis management for an organization confronting COVID-19. Among all the criteria, maintaining fixed reserves was the most successful factor regarding crisis management.

Research on the Effectiveness of Cyber Security Awareness in ICS Risk Assessment Frameworks

Assessing security awareness among users is essential for protecting industrial control systems (ICSs) from social engineering attacks. This research aimed to determine the effect of cyber security awareness on the emergency response to cyber security incidents in the ICS. Additionally, this study has adopted a variety of cyber security emergency response process measures and frameworks and comprehensively proposes a new organizational model of cyber security incident response. The corresponding measures are evaluated based on the MP2DR2 risk control matrix model to assess their practical value in the evaluation stage. This study found that after adding security awareness measures to response control measures, the influential value ranking of other control measures changed. The practical value of security awareness control measures was given a higher priority than that of other control measures. The research results highlight the importance of cyber security awareness and aim to inspire ICSs to place a higher priority on staff cyber security awareness in relation to cyber security incidents, which can effectively prevent the occurrence of cyber security incidents and make the field of industrial control application agency respond to incidents faster to restore the regular progress of all works.

A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks

Today’s advancements in wireless communication technologies have resulted in a tremendous volume of data being generated. Most of our information is part of a widespread network that connects various devices across the globe. The capabilities of electronic devices are also increasing day by day, which leads to more generation and sharing of information. Similarly, as mobile network topologies become more diverse and complicated, the incidence of security breaches has increased. It has hampered the uptake of smart mobile apps and services, which has been accentuated by the large variety of platforms that provide data, storage, computation, and application services to end-users. It becomes necessary in such scenarios to protect data and check its use and misuse. According to the research, an artificial intelligence-based security model should assure the secrecy, integrity, and authenticity of the system, its equipment, and the protocols that control the network, independent of its generation, in order to deal with such a complicated network. The open difficulties that mobile networks still face, such as unauthorised network scanning, fraud links, and so on, have been thoroughly examined. Numerous ML and DL techniques that can be utilised to create a secure environment, as well as various cyber security threats, are discussed. We address the necessity to develop new approaches to provide high security of electronic data in mobile networks because the possibilities for increasing mobile network security are inexhaustible.