Chataut, R., Nankya, M., & Akl, R. (2024). 6G networks and the AI Revolution—Exploring technologies, applications, and emerging challenges. Sensors, 24(6) doi:10.3390/s24061888
In the rapidly evolving landscape of wireless communication, each successive generation of networks has achieved significant technological leaps, profoundly transforming the way we connect and interact. From the analog simplicity of 1G to the digital prowess of 5G, the journey of mobile networks has been marked by constant innovation and escalating demands for faster, more reliable, and more efficient communication systems. As 5G becomes a global reality, laying the foundation for an interconnected world, the quest for even more advanced networks leads us to the threshold of the sixth-generation (6G) era. This paper presents a hierarchical exploration of 6G networks, poised at the forefront of the next revolution in wireless technology. This study delves into the technological advancements that underpin the need for 6G, examining its key features, benefits, and key enabling technologies. We dissect the intricacies of cutting-edge innovations like terahertz communication, ultra-massive MIMO, artificial intelligence (AI), machine learning (ML), quantum communication, and reconfigurable intelligent surfaces. Through a meticulous analysis, we evaluate the strengths, weaknesses, and state-of-the-art research in these areas, offering a wider view of the current progress and potential applications of 6G networks. Central to our discussion is the transformative role of AI in shaping the future of 6G networks. By integrating AI and ML, 6G networks are expected to offer unprecedented capabilities, from enhanced mobile broadband to groundbreaking applications in areas like smart cities and autonomous systems. This integration heralds a new era of intelligent, self-optimizing networks that promise to redefine the parameters of connectivity and digital interaction. We also address critical challenges in the deployment of 6G, from technological hurdles to regulatory concerns, providing a holistic assessment of potential barriers. By highlighting the interplay between 6G and AI technologies, this study maps out the current landscape and lights the path forward in this rapidly evolving domain. This paper aims to be a cornerstone resource, providing essential insights, addressing unresolved research questions, and stimulating further investigation into the multifaceted realm of 6G networks. By highlighting the synergy between 6G and AI technologies, we aim to illuminate the path forward in this rapidly evolving field. © 2024 by the authors.
Chataut, R., Usman, Y., Rahman, C. M. A., Gyawali, S., & Gyawali, P. K. (2024). Enhancing phishing detection with AI: A novel dataset and comprehensive analysis using machine learning and large language models. Paper presented at the 226–232. Retrieved from https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212705449&doi=10.1109%2fUEMCON62879.2024.10754710&partnerID=40&md5=742366f8ea2fbf9c8da0503f27ddab0d
Phishing emails are a significant threat to organizations, with over 90 % of cyber attacks starting from a malicious email. Despite built-in security measures, relying solely on these defenses can leave organizations vulnerable to cybercriminals who exploit human nature and the lack of tight security. Phishing emails, designed to deceive recipients into disclosing personal and financial information, represent a significant cybersecurity challenge. This paper introduces a comprehensive dataset curated explicitly for detecting phishing emails, featuring a collection of authentic and phishing emails. The dataset includes a broad spectrum of phishing techniques, such as sophisticated social engineering tactics, impersonation of reputable entities, and using urgent or threatening language to manipulate recipients. Phishing emails were collected to cover various scenarios, including financial fraud, account verification, and malware dissemination attempts. Our analysis involves a range of classical machine learning models alongside exploratory analysis with LLMs. The performance of these models was rigorously evaluated to furnish a comparative analysis of their detection capabilities. The dataset, one of the largest of its kind, offers a significant resource for researchers and cybersecurity professionals aiming to advance phishing detection methods. The dataset used in this research is publicly available, enabling further exploration and replication of the findings by the research community [1]. © 2024 IEEE.
Hogrefe, J., Cruz, E., Jaiswal, C., & Riofrio, J. (2024). AITracker: A neural network designed for efficient and affordable eye tracking. Paper presented at the 100–107. Retrieved from https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212669096&doi=10.1109%2fUEMCON62879.2024.10754664&partnerID=40&md5=8ee13ee7e20166d380176a56d7f9cf12
Eye-tracking technology has long been a cornerstone in both academic and research fields, offering insights into behavior, cognition, and visual perception. That being said, however, its accessibility is hindered by the high costs and proprietary nature of existing methodologies. To address these issues, we present AITracker, an open-source application that works using only a standard webcam, leveraging a deep-learning model in order to provide accurate eye-tracking capabilities. Our solution offers flexibility and adaptability, enabling users to customize individual parameters in the software to meet their specific needs. Through robust data collection and neural network training, AITracker achieves incredibly fast response times with a high degree of accuracy, enabling gaze-tracking in up to eight directions, as well as blink detection. In order to better understand the impact of this technology in the context of existing solutions, this paper compares AITracker's multi-layered neural network to various other prevalent eye-tracking methodologies. To that end, this paper also notes certain limitations that inhibit the software, including an undersized dataset and problematic distribution. Additionally, we explore various application scenarios, including hardware integration for assistive technology, hands-free gaming interfaces, advertising research, and attention monitoring in education. Moreover, feedback gathered from different users highlights the effectiveness and impact of AITracker across a diverse array of contexts. © 2024 IEEE.
Usman, Y., Gyawali, P. K., Gyawali, S., & Chataut, R. (2024). The dark side of AI: Large language models as tools for cyber attacks on vehicle systems. Paper presented at the 169–175. Retrieved from https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212685216&doi=10.1109%2fUEMCON62879.2024.10754676&partnerID=40&md5=f2f233145efc5caeaa7c701f11aa53c3
The rapid evolution of autonomous vehicles (AVs) presents significant opportunities for enhancing transportation safety and efficiency. However, with increasing connectivity and complex electronic systems, AVs also become vulnerable to cyberattacks. This paper investigates cybersecurity challenges in the realm of AVs, highlighting the role of artificial intelligence (AI), specifically Large Language Models (LLMs), in exploiting vulnerabilities. We analyze various attack vectors, including Controller Area Network (CAN) manipulation, Bluetooth vulnerabilities, and Key Fob hacking, emphasizing the need for proactive cybersecurity measures. Recent incidents, such as the remote compromise of various vehicle models, underscore the urgent need for robust security solutions in the automotive industry. By leveraging LLMs, attackers can craft sophisticated cyber threats targeting AVs, posing risks to both safety and privacy. We introduce HackerGPT, a customized LLM tailored for cyber attack generation, and demonstrate attacks on virtual CAN networks, Bluetooth systems, and Key Fobs. At the same time, our experiments reveal successful compromises in certain vehicle models; limitations exist, particularly in vehicles with advanced encryption and robust signal transmission protocols. However, this research underscores the broader need for increased awareness and proactive security measures in the automotive sector. Our findings aim to contribute significantly to the ongoing discourse on automotive cybersecurity, offering actionable insights for manufacturers and cybersecurity professionals to safeguard the future of mobility. © 2024 IEEE.
