Ransomware attacks have emerged as a major cyber-security threat wherein user data is encrypted upon system infection. Latest Ransomware strands using advanced obfuscation techniques along with offline C2 Server capabilities are hitting Individual users and big corporations alike. This problem has caused business disruption and, of course, financial loss. Since there is no such consolidated framework that can classify, detect and mitigate Ransomware attacks in one go, we are motivated to present Detection Avoidance Mitigation (DAM), a theoretical framework to review and classify techniques, tools, and strategies to detect, avoid and mitigate Ransomware. We have thoroughly investigated different scenarios and compared already existing state of the art review research against ours. The case study of the infamous Djvu Ransomware is incorporated to illustrate the modus-operandi of the latest Ransomware strands, including some suggestions to contain its spread.

Modern healthcare is a data-intensive domain representing an amalgamation of long-term electronic medical records, real-time patient monitoring data, and more recently sensor data from wearable computing. Blockchain in healthcare can address a multitude of challenges in healthcare, including care coordination, data security, and interoperability concerns, as technology advances. Technical challenges such as processing speed and massive data duplication will be resolved as improved technology. This data needs to be accessed seamlessly by a multitude of players from the general physicians to hospitals, medical service providers to insurance companies. Thus, healthcare-related data needs to be verified, securely stored, and shared while maintaining patient privacy and control over what portion of the data is shared, with whom it is shared, and how it is consumed. Blockchain has emerged as a technology stack of choice for distributed authentication, secure storage, and automated analysis of stored data in diverse domains including healthcare. Its distributed nature is a natural fit to the healthcare ecosystem with multiple participating entities and patients in different geographic locations. In this paper, we review the technology of blockchain to the healthcare domain analyzing and classifying work done in the field. Open challenges are identified and future directions for research are also presented.

The metaverse is touted as an exciting new technology amalgamation facilitating next-level immersive experiences for users. However, initial experiences indicate that a host of privacy, security and control issues will need to be effectively resolved for its vision to be realized. This paper highlights the security issues that will need to be resolved in the metaverse and the underlying enabling technologies/platforms. It also discussed the broader challenges confronting the developers, the service providers and other stakeholders in the metaverse ecosystem which if left unaddressed may hamper its broad adoption and appeal. Finally, some ideas on building a viable Zero-Trust Architecture (ZTA) model for the metaverse are presented.

The fake news “infodemic”, facilitated by social media and mobile message sharing platforms, has progressed from causing a nuisance to seriously impacting law and order through deliberate and large-scale manipulation of public sentiments. There are social, religious, political, and economic dimensions to the fake news phenomenon, providing enough motivation for interested parties to push biased opinions, claims, conspiracies and fraud to many naïve information consumers. The ease with which fake news can be created and propagated makes it extremely challenging to detect and mitigate. To combat the fake news, the researchers have utilized mechanisms which are largely based on Artificial Intelligence (AI) algorithms and social network analysis. However, no viable solution has yet been deployed at a scale. This paper present a comprehensive survey on combating fake news and evaluates the challenges involved in its detection with the help of existing detection mechanisms and techniques to control its spread. The challenges associated with combating fake news have been addressed based on the various aspects such as psychological, economic, and technical. Furthermore, we consider the fake news combat spectrum to analyze the stakeholder interventions due to the spread of fake news. Finally, various technology-based solutions have been presented for combating fake news and the associated future challenges and opportunities.

The Metaverse envisions a future where immersive online interactions become the norm. However, this vision can only be safely realized if user safety and privacy are prioritized. This review critically evaluates existing user safety and security measures within the Metaverse. We have highlighted the limited ability of existing solutions to protect against harassment, identity theft, and the misuse of personal data collected in this environment. This research comprehensively analyzes user safety and security dimensions within virtual worlds and presents a taxonomy to classify the unique threats and vulnerabilities Metaverse users may encounter. This review reveals critical gaps, including the lack of comprehensive security frameworks and balanced privacy-preserving models in virtual immersive environments. In response, we propose a novel Metaverse Security Architecture designed with Zero-Trust principles. This architecture prioritizes user control over data, identity, and experiences. It also emphasizes proactive security measures to mitigate diverse potential harms in virtual worlds. Our research highlights the critical importance of such robust, user-centric frameworks in enabling a Metaverse that is immersive, safe, and secure for all its participants.

Video surveillance and analytics solutions based on Artificial Intelligence (AI) are increasingly being deployed across industries, including academia. There are a number of use-cases for campus-wide video analytics applications. Detecting events of interest in real-time and generating alerts is a core requirement for such applications, making them both network and compute intensive. Thus, the underlying framework needs to be resource optimized in terms of latency, compute and storage requirements for a multitude of video applications. Increasingly privacy concerns have been voiced against the pervasive deployment of video analytics-based applications. Thus, protecting the privacy of students and staff in a campus setting shall be a major design consideration for such systems going forward. This paper presents a resource optimized and privacy preserving framework for campus-wide video analytics applications. Several use-cases are presented and early results from the deployment of the proposed framework establish its feasibility and effectiveness.