Making better IS security investment decisions: discovering the cost of data breach announcements during the COVID-19 pandemic

PurposeStock price reactions have often been used to evaluate the cost of data breaches in the current information systems (IS) security literature. To further this line of research, this study examines the impact of data breaches on stock returns, information asymmetry and unsystematic firm risk in the context of COVID-19.Design/methodology/approachThis paper employs an event study methodology and examines data breach events released in public databases, spanning pre- and post-COVID settings. This study investigated 283 data breaches of the US publicly traded firms, and the economic cost was measured by cumulative abnormal returns (CARs), trading volume, bid-ask spread and unsystematic risk.FindingsThe authors observe that data breaches during the COVID pandemic make investors react more negatively to data breach announcements, as reflected in the significantly negative difference in CARs between breached firms before COVID and those after COVID. The findings also indicate that, after the disclosure of data breach incidents, information asymmetry is reduced to a lesser extent compared with that in the pre-COVID setting. The authors also find that data breach events lead to an increase in the unsystematic risk of breached companies in the pre-COVID era but no change in the post-COVID era.Originality/valueThis study is the first effort to examine the economic consequences of data breaches by investigating the effects in the form of trading activities and risk measurement in the COVID setting.

Realtime Risk Monitoring System using Convolutional Neural Network for COVID19 Pandemic

The COVID19 pandemic has significantly changed the lifestyle of billions of people across the globe. It has greatly affected almost all sectors of business, industry and public life. As per the WHO's guidelines, wearing a face mask has become the new compulsory and precautionary measures for everyone. Currently, all the public and private service providers will expect their stakeholders to wear face mask in an appropriate way to avail any services. Therefore, detection of face mask at public places is a crucial task to help the society to overcome current pandemic. This paper presents a unique approach to not only detect face mask but also calculate the risk of getting infected by COVID-19 using machine learning algorithms. The proposed model detects the various faces present in an input video, identifies if it has a mask present or not. If the mask is not detected, the model calculates the risk of human being getting infected based on their age. Finally, the model generates the output and provides analysis based on the real time data it has processed. As a real-time surveillance system, the model can also classify a face when a person is moving in the live video. The proposed method attained a highest accuracy of 99.57 % against standard datasets under study. The authors experimented and explored various Convolutional Neural Network models like DenseNet, MobileNet_V2, Inception_V3 and YOLO_V4 find the best model, detecting the presence of masks accurately without causing over-fitting. © 2022 IEEE.

Lockdown, resilience and emergency statecraft in the Cape Town food system.

Well before the Covid-19 pandemic, rapidly growing cities of the global South were at the epicenter of multiple converging crises affecting food systems. Globally, government lockdown responses to the disease triggered shocks which cascaded unevenly through urban food systems, exacerbating food insecurity. Cities worldwide developed strategies to mitigate shocks, but research on statecraft enabling food systems resilience is sparse. Addressing this gap, we analyse the case of the African metropolis of Cape Town, where lockdown disrupted livelihoods, mobility and food provision, deepening food insecurity. Employing a vital systems security lens, we show how civil society and state networks mobilised to mitigate and adapt to lockdown impacts. Building on preceding institutional transformations, civil society and state collaborated to deliver emergency food aid, while advocacy networks raised food on the political agenda, formulated proposals, and navigated these through a widened policy window. Emergency statecraft assembled networks and regulatory instruments to secure food systems, enhance preparedness for future disruptions and present opportunities for transition towards more sustainable food systems. However, current food systems configuration enabled powerful actors to resist deeper transformation while devolving impacts to community networks. Despite resilient vested interests and power disparities, advocacy coalitions can anticipate and leverage crises to incrementally advance transformational, pro-poor statecraft.

Contactless Tool for COVID-19 Surveillance System

Due to the COVID-19 pandemic, surveillance systems have been implemented to monitor public health and trace the infected individuals. The Malaysian government has imposed the standard operating procedure (SOP) which includes checking of temperature for fever, use of hand sanitizers, and record their name, contact number, and date of attendance at points of entry. This paper proposes a contactless tool for COVID-19 surveillance that integrates all the 3 processes into one. The system carries out each stage in sequential order for every person, starting with checking temperature, dispensing hand sanitizer, and lastly data profiling record. The temperature is done using infrared thermometry that automatically adjusts to forehead height. Hand sanitizer is automatically dispensed when hands are detected under the pump. Image processing and optical character recognition are used to capture the name and contact number that will be shown on a tag carried by the individual and saved to the database. The process is contactless and requires no human operator, and yields accurate temperature data, works as intended while demonstrating high accuracy and speed in extracting information with optical character recognition. © 2021 IEEE.

Poll worker security: Assessment and design of usability and performance

Election infrastructure includes socio-technical systems that are designated as United States critical infrastructure within the Government Facilities sector. Following the 2016 United States' General Election and during the 2020 Presidential Election cycle, election security and the integrity of election processes became a prevalent, national conversation. From the 2019 U.S. Senate Intelligence Committee report indicating that election systems in all 50 states had been targeted by foreign adversaries to the more recent broadened use of, and concern about, mail-based voting during the COVID-19 pandemic, election integrity is increasingly important. Furthermore, poll workers play a crucial role in elections and election equipment, as they are one of the first lines of defense in systems security. This paper contributes to improving the security of election infrastructure through intentional, targeted, cyber, physical, and insider threat training for poll workers. Specifically, this paper details the engineering design, including pedagogical approach, and deployment of online, election-specific, threat training modules. Results of a System Usability Scale assessment from 44 poll workers indicate the content and online platform are easy to interact with and use. Further, the developed modules were piloted and then deployed in a mid-Atlantic state;participating counties include over 1,900 poll workers who serve nearly 750,000 voters. © 2021 IISE Annual Conference and Expo 2021. All rights reserved.