Cybersecurity and Privacy Challenges in Extended Reality: Threats, Solutions, and Risk Mitigation Strategies

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

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Anomaly Detection in Blockchain Transactions: A Machine Learning Approach within the Open Metaverse

Jurnal Informatika Ekonomi Bisnis, Год журнала: 2024, Номер unknown, С. 319 - 323

Опубликована: Апрель 15, 2024

This study investigates the application of machine learning models for anomaly detection and fraud analysis in blockchain transactions within Open Metaverse, amid growing complexity digital virtual spaces. Utilizing a dataset 78,600 that reflect broad spectrum user behaviors transaction types, we evaluated efficacy several predictive models, including RandomForest, LinearRegression, SVR, DecisionTree, KNeighbors, GradientBoosting, AdaBoost, Bagging, XGB, LightGBM, based on their Mean Cross-Validation Squared Error (Mean CV MSE). Our revealed ensemble methods, particularly RandomForest demonstrated superior performance with MSEs -0.00445 -0.00415, respectively, thereby highlighting robustness complex dataset. In contrast, LinearRegression SVR were among least effective, -224.67 -468.57, indicating potential misalignment dataset's characteristics. research underlines importance selecting appropriate strategies context showcasing need advanced, adaptable approaches. The findings contribute significantly to financial technology field, enhancing security integrity economic systems, advocate nuanced approach environments.

Язык: Английский

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The Future of Metaverse Security

Advances in information security, privacy, and ethics book series, Год журнала: 2024, Номер unknown, С. 253 - 279

Опубликована: Авг. 21, 2024

The metaverse, an evolving digital frontier integrating VR, AR, blockchain, 5G, and quantum computing, presents both opportunities security challenges. This chapter explores these emerging technologies, their implications, the innovative measures developed to mitigate associated risks. role of AI in enhancing metaverse is examined, highlighting effective algorithms future prospects. Challenges scaling security, regulatory ethical considerations, collaborative approaches are discussed. A vision for a secure inclusive proposed, emphasizing key characteristics strategies accessibility. concludes with essential takeaways directions security.

Язык: Английский

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Deep Guard-IoT: A Systematic Review of AI-Based Anomaly Detection Frameworks for Next-Generation IoT Security (2020-2024)

Wasit Journal of Pure sciences, Год журнала: 2024, Номер 3(4), С. 70 - 77

Опубликована: Дек. 30, 2024

The emergence of IoT devices has complicated the landscape cybersecurity in ways that had never been experienced before, thereby, giving raise to need for more developed methods can assist threat evaluation and deterrent. work international scope analyses particulars artificial intelligence-based anomaly detection technology implementation including perspectives recent period between 2020 2024 various architectures’ effectiveness their use low-resource environments. In this review, as well many other works carried out by authors, it is observed deep learning methods, such us Long Short-Term Memory (LSTM) networks GRU-LSTM hybrid models, achieved most accurate performance, ranging from 96% up 99.9% correct detection. Our examination focuses on several aspects security, challenges at device level, issues related security network data facets intelligence concepts architectures capable addressing challenges. results study state AI techniques tend be efficient have a high performance than conventional before but drawbacks realistic application owing lack adequate resources, absence standard practices sophistication new threats. also highlights gaps exist current approaches makes suggestions what done, importance developing

Язык: Английский

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