Topic 1: Machine Learning on Encrypted Data

Motivation: Machine learning (ML) is a powerful technique that can learn from data and provide insights, predictions, and recommendations for various domains and applications. However, ML often requires access to sensitive or confidential data, such as personal information, medical records, or financial transactions, which may pose privacy and security risks. How can we enable ML to operate on encrypted data without compromising its accuracy and efficiency? This is the main motivation for the research topic of machine learning for encrypted data, which explores novel cryptographic methods and algorithms that can perform ML tasks directly on ciphertexts, without revealing any information about the plaintexts. This research topic has significant implications for enhancing data protection and enabling privacy-preserving ML applications in various scenarios, such as cloud computing, federated learning, and edge computing.

Recent journal papers from our group on this topic:

C. H. Nguyen*, D. T. Hoang, D. N. Nguyen, K. Lauter, and M. Kim, ``Empowering AI with privacy: Homomorphic Encryption for Secure Deep Reinforcement Learning,'' Nature Machine Intelligence, accepted 2025.
B. D. Manh*, C. H. Nguyen*, D. T. Hoang, D. N. Nguyen, M. Zheng, and Q. V. Pham, ``Privacy-preserving cyberattack detection in blockchain-based IoT systems using Ai and Homomorphic Encryption,'' IEEE Internet of Things Journal, accepted 2025.
H. C. Nguyen*, Y. Saputra, D. T. Hoang, D. N. Nguyen, V. D. Nguyen, Y. Xiao, and E. Dutkiewicz, "Encrypted data caching and learning framework for robust federated learning-based mobile edge computing," IEEE/ACM Transactions on Networking, accepted 2024.

Datasets & Demos:

1) Mobile Edge Intelligence and Homomorphic Encryption to Secure Mobile AI Applications/Services: https://www.youtube.com/watch?v=NFw_nkQEvs0

Topic 2: Metaverse

Motivation: The Metaverse, envisioned as a collective virtual shared space, is emerging at the confluence of various advanced technologies, including virtual and augmented reality, blockchain, and artificial intelligence. This digital realm promises an unprecedented level of integration between virtual environments and the physical world, offering new dimensions to social interactions, economic transactions, and digital identities. However, the ambitious development of the Metaverse brings forth significant technical challenges, such as creating interoperable and scalable virtual spaces, ensuring real-time interactions with minimal latency, and developing secure and transparent systems for digital assets and identities. These technical hurdles underscore the urgent need for focused research to address the complexities of building a seamless, accessible, and secure Metaverse. Such research is crucial not only for unlocking the full potential of these virtual spaces but also for anticipating and mitigating the social, ethical, and security implications that accompany the creation of such expansive digital universes.

Recent journal papers from our group on this topic:

N. H. Chu*, H. Q. Nguyen*, D. N. Nguyen, D. T. Hoang, K. T. Phan, E. Dutkiewicz, D. Niyato, and T. Shu, ``Dynamic multi-tier resource allocation framework for Metaverse,'' IEEE Network, accepted 2024.
N. Q. Hieu*, D. T. Hoang, D. N. Nguyen, V. D. Nguyen, Y. Xiao, and E. Dutkiewicz, "Enhancing immersion and presence in the Metaverse with over-the-air brain-computer interface," IEEE TWC, accepted 2024.
N. Q. Hieu*, D. T. Hoang, D. N. Nguyen, and M. A. Alsheikh, "Reconstructing Human Pose from Inertial Measurements: A Generative Model-based Compressive Sensing Approach," IEEE JSAC, accepted 2024.
H. Y. Zhu, N. Q. Hieu*, D. T. Hoang, D. N. Nguyen, C. T. Lin, "A human-centric metaverse enabled by brain-computer interface: A survey," IEEE COMST, accepted 2024.
D. Tulone, D. T. Hoang, A. Puliafito, W. Y. B. Lim and P. Henz, "Design criteria and technical requirements for sustainable metaverse ecosystems," Technical Specification ITU FGMV-08, Oct. 2023.
D. T. Hoang, D. N. Nguyen, C. T. Nguyen*, E. Hossain, and D. Niyato, edt., Metaverse Communication and Computing Networks: Applications, Technologies, and Approaches. IEEE-Wiley, 2023.
C. T. Nguyen*, D. T. Hoang, D. N. Nguyen, Y. Xiao, D. Niyato, and E. Dutkiewicz, "MetaShard: A novel sharding blockchain platform for Metaverse applications," IEEE TMC, accepted 2023.
N. H. Chu*, D. T. Hoang, D. N. Nguyen, K. T. Phan, E. Dutkiewicz, D. Niyato, and T. Shu "MetaSlicing: A novel resource allocation framework for Metavserse," IEEE TMC, accepted 2023.
N. Q. Hieu*, D. N. Nguyen, D. T. Hoang, and E. Dutkiewicz, "When virtual reality meets rate splitting multiple access: A joint communication and computation approach," IEEE JSAC, vol. 41, no. 5, pp. 1536-1548, May 2023.

Topic 3: Machine Learning for Cyberattack Detection

Motivation: Cyberattacks are a serious threat to the security and privacy of information systems and networks. They can cause significant damage to individuals, organizations, and society by compromising data, disrupting services, and stealing resources. To prevent and mitigate cyberattacks, various methods have been proposed to detect and classify them in real time. Among these methods, machine learning (ML) has emerged as a powerful and effective technique, as it can learn from data and adapt to changing environments. However, applying ML to cyberattack detection also poses many technical challenges, such as dealing with imbalanced and noisy data, selecting appropriate features and algorithms, ensuring robustness and scalability, and providing explainability and trustworthiness. These challenges require novel solutions and rigorous research from different disciplines and perspectives. Therefore, the research on machine learning for cyberattack detection is motivated by both the practical need to enhance cybersecurity and the scientific interest to advance ML theory and applications.

Recent journal papers from our group on this topic:

T. V. Khoa*, et al., "Securing Blockchain Systems: A Novel Collaborative Learning Framework to Detect Attacks in Transactions and Smart Contracts," under review.
P. V. Dinh*, Q. U. Nguyen, D. T. Hoang, D. N. Nguyen, S. P. Bao, and E. Dutkiewicz, "Twin auto-encoder for learning separable representation in cyberattack detection," IEEE TSC, under review.
T. V. Khoa*, D. H. Son, D. T. Hoang, N. L. Trung, T. T. T. Quynh, D. N. Nguyen, N. V. Ha, E. Dutkiewicz, "Collaborative learning for cyberattack detection in blockchain networks," IEEE Transactions on Systems, Man, and Cybernetics, accepted 2024.
P. V. Dinh*, Q. U. Nguyen, D. T. Hoang, D. N. Nguyen, S. P. Bao, and E. Dutkiewicz, "Constrained twin variational auto-encoder for intrusion detection in IoT systems,"IEEE Internet of Things Journal, accepted 2023.
T. V. Khoa*, D. T. Hoang, N. L. Trung, C. T. Nguyen*, T. T. T. Quynh, D. N. Nguyen, N. V. Ha, and E. Dutkiewicz, "Deep transfer learning: A novel collaborative learning model for cyberattack detection systems in IoT networks," IEEE Internet of Things Journal, vol. 10, no. 10, pp. 8578 - 8589, May 2023.
L. Vu*, Q. U. Nguyen, D. N. Nguyen, D. T. Hoang, and E. Dutkiewicz, "Deep generative learning models for cloud intrusion detection systems," IEEE Transactions of Cybernetics, vol. 53, no. 1, pp. 565-577, Jan. 2023.
L. Vu*, V. L. Cao, Q. U. Nguyen, D. N. Nguyen, D. T. Hoang, and E. Dutkiewicz, "Learning latent representation for IoT anomaly detection," IEEE Transactions of Cybernetics, vol. 52, no. 5, pp. 3769-3782, May 2022.

Datasets & Demos:

1) Blockchain Network Attack Traffic dataset (BNat): https://avitech-vnu.github.io/BNaT/#/

2) Blockchain Transaction-based Attacks Dataset (BTAT): https://avitech-vnu.github.io/BTAT/#/

Topic 4: Blockchain

Motivation: Blockchain technology has emerged as a transformative innovation with the potential to revolutionize industries by providing decentralized, secure, and transparent systems for data and transaction management. Its significance lies in the ability to ensure trust without relying on intermediaries, making it highly valuable for applications in finance, supply chain, healthcare, and beyond. However, despite its promise, blockchain faces several challenges that hinder its wider adoption. Scalability issues, energy inefficiency, and slow transaction processing times present technical hurdles, while security threats, such as the rise of sophisticated attacks like double-spending, Sybil attacks, and the vulnerability of smart contracts, pose significant risks. These challenges motivate my research to develop more robust, scalable, and secure blockchain systems that can withstand evolving cyber threats and meet the demands of critical real-world applications.

Recent journal papers from our group on this topic:

C. T. Nguyen*, D. T. Hoang, D. N. Nguyen, E. Dutkiewicz, L. Le, and R. Joyce, edt., Proof-of-Stake for Blockchain Networks: Fundamentals, Challenges and Approaches, Institution of Engineering and Technology (IET), 2024.
C. T. Nguyen, Y. Liu, H. Du, D. T. Hoang, D. Niyato, D. Nguyen, and S. Mao, "Generative AI-enabled blockchain networks: Fundamentals, applications, and case study," IEEE Network, accepted 2024.
T. V. Khoa*, D. H. Son, D. T. Hoang, N. L. Trung, T. T. T. Quynh, D. N. Nguyen, N. V. Ha, E. Dutkiewicz, "Collaborative learning for cyberattack detection in blockchain networks," IEEE Transactions on Systems, Man, and Cybernetics, accepted 2024.
C. T. Nguyen*, D. T. Hoang, D. N. Nguyen, Y. Xiao, D. Niyato, and E. Dutkiewicz, "MetaShard: A novel sharding blockchain platform for Metaverse applications," IEEE Transactions on Mobile Computing, accepted 2023.
C. T. Nguyen*, D. T. Hoang, D. N. Nguyen, Y. Xiao, H. A. Pham, E. Dutkiewicz and N. H. Tuong, "FedChain: Secure proof-of-stake-based framework for federated-blockchain systems," IEEE Transactions on Services Computing, vol. 16, no. 4, pp. 2642-2656, Aug. 2023.
C. T. Nguyen*, D. N. Nguyen, D. T. Hoang, H.-A. Pham, N. H. Tuong, Y. Xiao, and E. Dutkiewicz, "BlockRoam: Blockchain-based roaming management system for future mobile networks," IEEE Transactions on Mobile Computing, vol. 21, no. 11, pp. 3880-3894, Nov. 2022.
C. T. Nguyen*, D. T. Hoang, D. N. Nguyen, D. Niyato, H. T. Nguyen, and E. Dutkiewicz, "Proof-of-Stake consensus mechanisms for future blockchain networks: Fundamentals, applications, and opportunities," IEEE Access, vol. 37, no. 11, pp. 2603-2620, Nov. 2019.
W. Wang, D. T. Hoang, P. Hu, Z. Xiong, D. Niyato, P. Wang, Y. Wen, and D. I. Kim, "A survey on consensus mechanisms and mining strategy management in blockchain networks," IEEE Access, vol. 7, pp. 22328-22370, Jan. 2019.