Michail Tsikerdekis

The elderly population is expected to grow worldwide due to significant improvements in health care, infrastructure, and economic factors. With longer lifespans, the elderly face health challenges associated with advanced age, chronic diseases, and disabilities. The Metaverse is a technology that has emerged in recent years with the potential to help manage elderly health challenges. We describe the basic components of the Metaverse and the services it currently provides. Then, we present and conceptually discuss use cases of the Metaverse (and associated technology) for elderly health care. Finally, we discuss various challenges that must be addressed in the future to successfully deploy elderly health-care Metaverse applications.

The integration of artificial intelligence has led to significant advancements across industries but also exposed systems to security vulnerabilities. We evaluate defense methods, including robust data practices, adversarial training, model hardening, fairness-aware algorithms, and privacy-preserving techniques, and highlight each method’s effectiveness in addressing specific vulnerabilities.

In recent years, we have witnessed growing interest in using deep learning to detect misinformation. This increased attention is being driven by deep learning technologies’ ability to accurately detect this misinformation. However, there is a diverse array of content that can be considered misinformation, such as fake news and satire. Similarly, in the field of deep learning, there are several architectures with variable efficacy depending on the context and data involved. This study aims to highlight the various types of misinformation attacks and deep learning architectures that are used to detect them. Based on our selection of the recent literature, we present a classification of deep learning approaches and their relative effectiveness in detecting misinformation, along with their limitations in terms of accuracy as well as computational overhead. Finally, we discuss some challenges and limitations that arise FROM the use of deep learning architectures in misinformation detection.

Network anomaly detection solutions can analyze a network’s data volume by protocol over time and can detect many kinds of cyberattacks such as exfiltration. We use exponential random graph models (ERGMs) in order to flatten hourly network topological characteristics into a time series, and Autoregressive Moving Average (ARMA) to analyze that time series and to detect potential attacks. In particular, we extend our previous method in not only demonstrating detection over hourly data but also through labeling of nodes and over the HTTP protocol. We demonstrate the effectiveness of our method using real-world data for creating exfiltration scenarios. We highlight how our method has the potential to provide a useful description of what is happening in the network structure and how this can assist cybersecurity analysts in making better decisions in conjunction with existing intrusion detection systems. Finally, we describe some strengths of our method, its accuracy based on the right selection of parameters, as well as its low computational requirements.

Network anomaly detection solutions are being used as defense against several attacks, especially those related to data exfiltration. Several methods exist in the literature, such as clustering or neural networks. However, these methods often focus on local and global network indicators instead of network structural properties, such as understanding which devices typically communicate with other devices. To address this literature gap, we propose a method that uses exponential random graph modeling to integrate network topology structure statistics in anomaly detection. We demonstrate the effectiveness of our method using real-world examples as a baseline for experiments on domain name system (DNS) data exfiltration scenarios. We highlight how our method provides better insight into how network traffic may alter network graph structure and how this can assist cybersecurity analysts in making better decisions in conjunction with existing intrusion detection systems. Finally, we compare and contrast the accuracy, false positive rate and computational overhead of our method with other methods.

Marine network protocols are domain-specific network protocols that aim to incorporate particular features within the specialized marine context that devices are implemented in. Devices implemented in such vessels involve critical equipment; however, limited research exists for marine network protocol security. In this paper, we provide an analysis of several marine network protocols used in today’s vessels and provide a classification of attack risks. Several protocols involve known security limitations, such as Automated Identification System (AIS) and National Marine Electronic Association (NMEA) 0183, while newer protocols, such as OneNet provide more security hardiness. We further identify several challenges and opportunities for future implementations of such protocols.

Bots in video games has been gaining the interest of industry as well as academia as a problem that has been enabled by the recent advances in deep learning and reinforcement learning. In turn several studies have attempted to establish bot detectors in various video games. In this article, we introduce a bot detection model that can implemented in real-time and provide feedback on whether a player that is being observed is a bot or human. The model uses a limited feature set and amount of time of observation in order to be small and generalize easily to other domains. We trained and tested our model in a series of replays for Starcraft: Brood War and have yielded a higher accuracy than past studies and a fraction of detection time.

In the last few years, we have witnessed an explosive growth of fake content on the Internet which has significantly affected the veracity of information on many social platforms. Much of this disruption has been caused by the proliferation of advanced machine and deep learning methods. In turn, social platforms have been using the same technological methods in order to detect fake content. However, there is understanding of the strengths and weaknesses of these detection methods. We describe examples of machine and deep learning approaches that can be used to detect different types of fake content. We also discuss the characteristics and the potential for adversarial attacks on these methods that could reduce the accuracy of fake content detection. Finally, we identify and discuss some future research challenges in this area.

Identity deception in online social networks is a pervasive problem. Ongoing research is developing methods for identity deception detection. However, the real-world efficacy of these methods is currently unknown because they have been evaluated largely through laboratory experiments. We present a review of representative state-of-the-art results on identity deception detection. Based on this analysis, we identify common methodological weaknesses for these approaches, and we propose recommendations that can increase their effectiveness for when they are applied in real-world environments.