Analysis of retail sector research evolution and trends during COVID-19.

The purpose of this study is to analysis the evolution of the retail sector during the COVID-19 period and to identify future research issues. Scopus databases were searched for articles published in English between 2020 and 2022 to discover current trends and concerns in the retail industry. A total of 1071 empirical and nonempirical studies were compiled as a result of the evaluation process. During the study period, the number of articles published in scientific journals increased exponentially, indicating that the research topic is still in the developmental phase. It also highlights the most important research trends, allowing numerous new research lines to be proposed via visual mapping of Thematic Maps. This study makes an important contribution to the field of the retail sector, providing a comprehensive overview of the field's evolution and current status, as well as a comprehensive, synthesized, and organized summary of the various perspectives, definitions, and trends in the field.

A novel approach for DDoS attacks detection in COVID-19 scenario for small entrepreneurs.

The current COVID-19 issue has altered the way of doing business. Now that most customers prefer to do business online, many companies are shifting their business models, which attracts cyber attackers to launch several kinds of cyberattacks against commercial companies simultaneously. The most common and lethal DDoS attack disables the victim's online resources. While large businesses can afford defensive measures against DDoS assaults, the situation is different for new entrepreneurs. Their lack of security resources restricts their ability to ward off DDoS attacks. Here, we aim to highlight the problems that prospective entrepreneurs should be aware of before joining the business, followed by a filtering mechanism that efficiently identifies DDoS assaults in the COVID-19 scenario, which is the subject of our research. The suggested approach employs statistical and machine learning techniques to discriminate between DDoS attack data and regular communication. Our suggested framework is cost-effective and identifies DDoS attack traffic with a 92.8% accuracy rate.