Cybersecurity and Privacy Challenges in Extended Reality: Threats, Solutions, and Risk Mitigation Strategies DOI Creative Commons
Mohammed El‐Hajj

Virtual Worlds, Год журнала: 2024, Номер 4(1), С. 1 - 1

Опубликована: Дек. 30, 2024

Extended Reality (XR), encompassing Augmented (AR), Virtual (VR), and Mixed (MR), enables immersive experiences across various fields, including entertainment, healthcare, education. However, its data-intensive interactive nature introduces significant cybersecurity privacy challenges. This paper presents a detailed adversary model to identify threat actors attack vectors in XR environments. We analyze key risks, identity theft behavioral data leakage, which can lead profiling, manipulation, or invasive targeted advertising. To mitigate these we explore technical solutions such as Advanced Encryption Standard (AES), Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) for secure transmission, multi-factor biometric authentication, anonymization techniques, AI-driven anomaly detection real-time monitoring. A comparative benchmark evaluates solutions’ practicality, strengths, limitations applications. The findings emphasize the need holistic approach, combining robust measures with privacy-centric policies, ecosystems ensure user trust.

Язык: Английский

Advancing Brain-Computer Interface Closed-Loop Systems for Neurorehabilitation: A Systematic Review of AI and Machine Learning Innovations in Biomedical Engineering (Preprint) DOI
Christopher B. Williams, Fahim Islam Anik, Md. Mehedi Hasan

и другие.

Опубликована: Фев. 5, 2025

BACKGROUND Background: Brain-Computer Interface (BCI) closed-loop systems have emerged as a promising tool in healthcare and wellness monitoring, particularly neurorehabilitation cognitive assessment. With the increasing burden of neurological disorders, including Alzheimer’s Disease Related Dementias (AD/ADRD), there is critical need for real-time, non-invasive monitoring technologies. BCIs enable direct communication between brain external devices, leveraging artificial intelligence (AI) machine learning (ML) to interpret neural signals. However, challenges such signal noise, data processing limitations, privacy concerns hinder widespread implementation. This review explores integration ML AI BCI systems, evaluating their effectiveness improving assessments interventions. OBJECTIVE Objective: The primary objective this study investigate role enhancing applications. Specifically, we aim analyze methods parameters used these assess different techniques, identify key development implementation, propose framework utilizing longitudinal AD/ADRD patients. By addressing aspects, seeks provide comprehensive overview potential limitations AI-driven healthcare. METHODS Methods: A systematic literature was conducted following PRISMA guidelines, focusing on studies published 2019 2024. Research articles were sourced from PubMed, IEEE, ACM, Scopus using predefined keywords related BCIs, AI, AD/ADRD. total 220 papers initially identified, with 18 meeting final inclusion criteria. Data extraction followed structured matrix approach, categorizing based methods, algorithms, proposed solutions. comparative analysis performed synthesize findings trends AI-enhanced monitoring. RESULTS Results: identified several Transfer Learning, Support Vector Machines, Convolutional Neural Networks, that enhance performance. These improve classification, feature extraction, real-time adaptability, enabling accurate states. long calibration sessions, computational costs, security risks, variability signals also highlighted. To address issues, emerging solutions improved sensor technology, efficient protocols, advanced decoding models are being explored. Additionally, show alert support caregivers managing CONCLUSIONS Conclusions: when integrated ML, offer significant advancements healthcare, neurorehabilitation. Despite potential, accuracy, security, scalability must be addressed clinical adoption. Future research should focus refining models, processing, user accessibility. continued advancements, AI-powered can revolutionize personalized by providing continuous, adaptive intervention patients disorders.

Язык: Английский

Процитировано

0
Exploring the Uncoordinated Privacy Protections of Eye Tracking and VR Motion Data for Unauthorized User Identification DOI
Samantha Aziz, Oleg V. Komogortsev

Опубликована: Март 8, 2025

Язык: Английский

Процитировано

0
Cybersecurity and Privacy Challenges in Extended Reality: Threats, Solutions, and Risk Mitigation Strategies DOI Creative Commons
Mohammed El‐Hajj

Virtual Worlds, Год журнала: 2024, Номер 4(1), С. 1 - 1

Опубликована: Дек. 30, 2024

Extended Reality (XR), encompassing Augmented (AR), Virtual (VR), and Mixed (MR), enables immersive experiences across various fields, including entertainment, healthcare, education. However, its data-intensive interactive nature introduces significant cybersecurity privacy challenges. This paper presents a detailed adversary model to identify threat actors attack vectors in XR environments. We analyze key risks, identity theft behavioral data leakage, which can lead profiling, manipulation, or invasive targeted advertising. To mitigate these we explore technical solutions such as Advanced Encryption Standard (AES), Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) for secure transmission, multi-factor biometric authentication, anonymization techniques, AI-driven anomaly detection real-time monitoring. A comparative benchmark evaluates solutions’ practicality, strengths, limitations applications. The findings emphasize the need holistic approach, combining robust measures with privacy-centric policies, ecosystems ensure user trust.

Язык: Английский

Процитировано

1