Philanthropic sales in live-streaming shopping: The impact of online interaction on consumer impulse buying.

As philanthropic sales via live-streaming shopping have played an important role in alleviating the huge backlog of agricultural products during the outbreak of the COVID-19 pandemic, this paper aims to study how online interaction in philanthropic marketing exerts influence on consumer impulse buying behaviors. We empirically explore four major dimensions of online interactions in philanthropic live-streaming sales, i.e., the live streamers' image, the herd effect of consumers, the responsiveness of sellers, and the mutual trust between consumers. The results reveal that the herd effect of consumers and the responsiveness of sellers could promote consumers' empathy ability toward the growers of the products sold lively, whereas the live streamers' image and the mutual trust between consumers have little effect on empathy promotions. Meanwhile, both the consumers' empathy ability and the live streamers' image positively affect consumers' impulse buying behavior, which suggests a partial moderating role of consumers' empathy ability. Lastly, by taking both social and business perspectives, we provide managerial implications for improving the effectiveness and efficiency of philanthropic live-streaming sales to alleviate social and economic pressure in emergencies.

A statistical approach to secure health care services from DDoS attacks during COVID-19 pandemic.

Over the course of this year, more than a billion people have been afflicted by the COVID-19 outbreak. As long as individuals maintain their social distance, they should all be secure at this period. Because of this, there has been a rise in the usage of different online technologies, but at the same time, there has also been a rise in the likelihood of different cyber-attacks. A DDoS assault, the most prevalent and deadly of them all, impairs an online resource for its users. Thus, in this paper, we have proposed a filtering approach that can work efficiently in the COVID-19 scenario and detect the DDoS attack. We base our proposed approach on statistical methods like packet score and entropy variation for the identification of DDoS attack traffic. We have implemented our proposed approach on Omnet++ and for testing its efficiency we have checked it with different test cases. Our proposed approach detects the DDoS attack traffic with 96% accuracy and can also clearly have differentiated the DDoS attack traffic from the flash crowd.