Integrating the internet of things (IoT) in medical applications has significantly improved healthcare operations and patient treatment activities. Real-time patient monitoring and remote diagnostics allow the physician to serve more patients and save human lives using internet of medical things (IoMT) technology. However, IoMT devices are prone to cyber attacks, and security and privacy have been a concern. The IoMT devices operate on low computing and low memory, and implementing security technology on IoMT devices is not feasible. In this article, we propose particle swarm optimization deep neural network (PSO-DNN) for implementing an effective and accurate intrusion detection system in IoMT. Our approach outperforms the state of the art with an accuracy of 96% to detect network intrusions using the combined network traffic and patient’s sensing dataset. We also present an extensive analysis of using various Machine Learning(ML) and Deep Learning (DL) techniques for network intrusion detection in IoMT and confirm that DL models perform slightly better than ML models.

People all over the world were under severe stress and were concerned about their health after a devastating pandemic struck the world in the form of a novel coronavirus disease (COVID-19) in late December 2019. Many nations imposed strict lockdowns and quarantines, causing citizens to maintain social isolation, throwing many companies to a halt. Thousands of people took to Twitter during these challenging circumstances to express their feelings about being caught in the middle of a storm. Twitter witnessed an outpouring of emotions ranging from fear, anger, and sadness associated with the spread of a novel virus that has no known cure, to voices of support and trust for nations’ official response to the pandemic. In studying the emotional response (anger, fear, and sadness) on Twitter about the COVID-19 crisis, we thus see a tale of two crises unfold—choosing health or economy. We capture collective emotions on social media and investigate the patterns and impact of these negative emotions during various stages of the disease outbreak. It also provides crucial insights to health officials and government agencies on communicating crisis information to the public via social media.

In the United States and around the world, gun violence has become a long-standing public safety concern and a security threat, due to violent gun-related crimes, injuries, and fatalities. Although legislators and lawmakers have attempted to mitigate its threats through legislation, research on gun violence confirms the need for a comprehensive approach to gun violence prevention. This entails addressing the problem in as many ways as possible, such as through legislation, new technological advancements, re-engineering supply, and administrative protocols, among others. The research focuses on the technological, supply, and administrative aspects, in which we propose a manner of managing gun-related data efficiently from the point of manufacture/sale, as well as at points of transfers between secondary sellers for the improvement of criminal investigation processes. Making data more readily available with greater integrity will facilitate successful investigations and prosecutions of gun crimes. Currently, there is no single and uniform platform for firearm manufacturers, dealers, and other stakeholders involved in firearm sales, dissemination, management, and investigation. With the help of Blockchain technology, gun registry, ownership, transfers, and, most importantly, investigations, when crimes occur, can all be managed efficiently, breaking the cycle of gun violence. The identification of guns, gun tracing, and identification of gun owners/possessors rely on accuracy, integrity, and consistency in related systems to influence gun crime investigation processes. Blockchain technology, which uses a consensus-based approach to improve processes and transactions, is demonstrated in this study as a way to enhance these procedures. To the best of our knowledge, this is the first study to explore and demonstrate the utility of Blockchain for gun-related criminal investigations using a design science approach.

The cyber-physical system (CPS) has made tremendous progress in recent years and also disrupting technical fields ranging from health, transportation, industries, and more. However, CPS security is still one of the concerns for wide adoption owing to the high number of devices connecting to the internet and the traditional security solutions may not be suitable to protect the advanced, application-specific attacks. This paper presents a programmable device network layer architecture to combat attacks and efficient network monitoring in heterogeneous environment CPS applications. The authors leverage industrial control systems (ICS) to discuss the existing issues, highlighting the importance of advanced network layers for CPS. The programmable data plane language (P4) is introduced to detect well known HELLO flood attacks with minimal effort in the network level and show that programmable switches are suitable to implement security solutions in CPS applications.