CHALLENGES AND BARRIERS TO DIGITAL FORENSICS IN THE CLOUD

Cloud computing provides individuals and organizations affordable access to various resources such as storage, servers, computing power, and software among others. The growing use of this decentralized approach presents many opportunities for cost and process optimization but at the same time it brings new challenges and barriers when it comes to solving crimes in the digital realm. For example, the cloud provides redundancy by making multiple copies of the data at various locations across the world. There are currently a lot of discussions regarding the ownership of the data on the cloud and jurisdiction issues because of the decentralized redundancy. So, when a crime occurs and data on the cloud is compromised, this brings up the problem of digital forensic investigations on third party networks and resources. While technology is progressing incredibly fast, policy makers tend to lag behind and not provide law enforcement with the necessary tools to solve some of these new 21st century crimes. The current paper provides an overview of some of the major challenges and barriers to digital forensic investigations involving the cloud. It offers recommendations for overcoming them and discusses directions for future research

Exploring the Shift from Physical to Cybercrime at the Onset of the COVID-19 Pandemic

The novel coronavirus has made an impact on virtually every aspect of our lives. The current study utilizes secondary data to identify patterns and trends related to shifting crime from the physical to the cyber domain. With millions, if not billions, people staying at home, attackers now look for new ways to commit crimes. Our findings indicate that while a lot of crimes such as robbery, assault, rape, and murder have declined at the beginning of the pandemic, we are also witnessing a rise in cybercrime, vehicle theft, and domestic violence. The current study looks specifically at phishing and what new trends are observed due to COVID-19. The current work is grounded in routine activity theory and demonstrates its relevance to both the physical and cyberspace. The implications of our work can be used by scholars who want to continue researching this new phenomenon. Practitioners can utilize our findings to look for ways to improve the corporate security posture by protecting the employees and customers working from home. Developing new phishing training and awareness programs should be focused around possible scenarios involving COVID-19. Our study suggests victims are more likely to fall prey to those during times of fear and uncertainty like the current pandemic

Application of Social Matching Recommender Systems in Healthcare

Recommender systems are software tools and techniques that give suggestions for items to be of interest to a user. The suggestions provided are aimed at supporting decision-making processes. The application of recommender systems in healthcare is expansive, ranging improving doctors’ appointments (Zini and Ricci 2011), providing personalized recommendations (Wiesner and Pfeifer 2010), and diagnosing diseases (Hussein et al. 2012). Mostly, these systems are based on conventional algorithms such as collaborative filtering and content-based recommendations. Although such tools and technologies have made a difference, they do not address the healthcare patient-physician matching issue at its core. As an issue that requires people-to-people interactions, the concept of social matching is more suitable. The concept posits that both users and items are as dynamic as they ought to be. This view denotes a shift in perspectives, leading to changes in strategic and implementation approaches to solve the patient-physician matching issue. Such a new system starts when a patient signs up with the insurer. The patient provides personal, geographical, and demographic data in the system. To collect more information, an optional questionnaire is often provided. This questionnaire voluntarily extracts additional information such as patients’ care preferences and health issues. Specifically, attributes such as preexisting conditions, current medications, age, gender, and language(s) spoken would shed light on patients’ current needs. Correspondingly, the system extracts all physicians’ relevant information from the data warehouse. The questionnaire also asks the patient’s preferences in several predefined categories, namely physician’s availability (e.g., operating hours and dates, expertise), accessibility (e.g., location, utilization rate), and demographics (e.g., gender, language spoken). The recommender system processes the questionnaire and derives appropriate weights for each preference. Afterward, the recommender system assigns matching scores to physicians, with the highest match on the top. The matching score is hidden from the patient to prevent misinterpretation. However, confirming phrases such as “Doctor X could speak your preferred language†and/or “Doctor X is specialized in caring for your needs†indicate in an understandable format why the system suggests a physician to the patient. The system can incorporate human-in-the-loop practices allowing users to express their own preferences at the time of matching. After the patient selects the physician, the system will set up the first appointment so the patient and the physician can meet to establish a formal relationship. After the appointment is concluded, additional data will be reviewed to assess the efficacy of the match, thereby creating a feedback loop to improve the matching algorithm. We are currently developing a system prototype that can meet these goals with a primary focus on fostering a positive, long-lasting relationship between patients and providers based on the social matching concept. Moreover, the system seeks to strike a balance of overall provider utilization in the network. The more patients a physician manages, the less effective the care becomes. On the other hand, having too few patients per physician breeds inefficiency, thereby escalating healthcare costs. As a result, while patient care and patient satisfaction are the primary concern, the recommender system has to maximize the collective physicians’ utilization rate to improve the effectiveness of the network, leading to overall cost reduction. This is a first step in using recommender systems for improving healthcare outcomes and has the potential to revolutionize the field. Any new implementations will also have to demonstrate highest standards of protecting patient information and ensuring user privacy

A Taxonomy for Risk Assessment of Cyberattacks on Critical Infrastructure (TRACI)

Cybercrime against critical infrastructure such as nuclear reactors, power plants, and dams has been increasing in frequency and severity. Recent literature regarding these types of attacks has been extensive but due to the sensitive nature of this field, there is very little empirical data. We address these issues by integrating Routine Activity Theory and Rational Choice Theory, and we create a classification tool called TRACI (Taxonomy for Risk Assessment of Cyberattacks on Critical Infrastructure). We take a Design Science Research approach to develop, evaluate, and refine the proposed artifact. We use mix methods to demonstrate that our taxonomy can successfully capture the characteristics of various cyberattacks against critical infrastructure. TRACI consists of three dimensions, and each dimension contains its own subdimensions. The first dimension comprises of hacker motivation, which can be financial, socio-cultural, thrill-seeking, and/or economic. The second dimension represents the assets such as cyber, physical, and/or cyber-physical components. The third dimension is related to threats, vulnerabilities, and controls that are fundamental to establishing and maintaining an information security posture and overall cyber resilience. Our work is among the first to utilize criminological theories and Design Science to create an empirically validated artifact for improving critical infrastructure risk management

Using Design Science Research to Develop a Secure Social Platform for Complementary and Alternative Medicine

Complementary and alternative medicine (CAM) practices are being used by a growing number of individuals. However, many patients do not disclose this information to their physicians, which can lead to ineffective or even harmful treatment. Social platforms and mobile applications are an efficient approach to bridge this communication gap between patients, CAM practitioners, and western medicine physicians. We utilize a design science approach to design, build, and evaluate a secure CAM social platform. We demonstrate the utility and value of the tool using a Systems Usability Scale and data from Google Analytics. The current study identifies gaps in patient-physician communication related to CAM disclosure and provides an empirically validated and secure tool to improve the process. Further, it demonstrates how a social platform can organize more efficiently the efforts related to successful CAM communication. The study also identifies best practices in designing and developing mechanisms for patient engagement and empowerment

A Taxonomy of Cyberattacks against Critical Infrastructure

The current study proposes a taxonomy to organize existing knowledge on cybercrimes against critical infrastructure such as power plants, water treatment facilities, dams, and nuclear facilities. Routine Activity Theory is used to inform a three-dimensional taxonomy with the following dimensions: hacker motivation (likely offender), cyber, physical, and cyber-physical components of any cyber-physical system (suitable target), and security (capable guardian). The focus of the study is to develop and evaluate the classification tool using Design Science Research (DSR) methodology. Publicly available data was used to evaluate the utility and usability of the proposed artifact by exploring three possible scenarios – Stuxnet, the Ukrainian power grid shut down, and ransomware attacks. While similar taxonomies exist, none of them have been verified due to the sensitive nature of the data and this would be one of the first empirically validated frameworks to explore cyberattacks against critical infrastructure. By better understanding these attacks, we can be better prepared to prevent and respond to incidents