no code implementations • 22 Mar 2024 • Phai Vu Dinh, Quang Uy Nguyen, Thai Hoang Dinh, Diep N. Nguyen, Bao Son Pham, Eryk Dutkiewicz
The output of TAE called the \textit{reconstruction representation} is input to downstream models to detect cyberattacks.
no code implementations • 22 Mar 2024 • Phai Vu Dinh, Diep N. Nguyen, Dinh Thai Hoang, Quang Uy Nguyen, Eryk Dutkiewicz, Son Pham Bao
The MIAE model is trained in an unsupervised learning mode to transform the heterogeneous inputs into lower-dimensional representation, which helps classifiers distinguish between normal behaviour and different types of attacks.
no code implementations • 5 Dec 2023 • Phai Vu Dinh, Quang Uy Nguyen, Dinh Thai Hoang, Diep N. Nguyen, Son Pham Bao, Eryk Dutkiewicz
Intrusion detection systems (IDSs) play a critical role in protecting billions of IoT devices from malicious attacks.
no code implementations • 27 Feb 2023 • Nam H. Chu, Diep N. Nguyen, Dinh Thai Hoang, Khoa T. Phan, Eryk Dutkiewicz, Dusit Niyato, Tao Shu
This work proposes a novel framework to dynamically and effectively manage and allocate different types of resources for Metaverse applications, which are forecasted to demand massive resources of various types that have never been seen before.
no code implementations • 17 Dec 2022 • Nguyen Quang Hieu, Dinh Thai Hoang, Diep N. Nguyen, Eryk Dutkiewicz
Our proposed framework involves a mixed decision-making and classification problem in which the base station has to allocate its computing and radio resources to the users and classify the brain signals of users in an efficient manner.
no code implementations • 14 Nov 2022 • Hai M. Nguyen, Nam H. Chu, Diep N. Nguyen, Dinh Thai Hoang, Van-Dinh Nguyen, Minh Hoang Ha, Eryk Dutkiewicz, Marwan Krunz
This theoretical bound is decomposed into two components, including the variance of the global gradient and the quadratic bias that can be minimized by optimizing the communication resources, and quantization/noise parameters.
no code implementations • 21 Mar 2022 • Tran Viet Khoa, Do Hai Son, Dinh Thai Hoang, Nguyen Linh Trung, Tran Thi Thuy Quynh, Diep N. Nguyen, Nguyen Viet Ha, Eryk Dutkiewicz
The main idea of the proposed learning model is to enable blockchain nodes to actively collect data, share the knowledge learned from its data, and then exchange the knowledge with other blockchain nodes in the network.
no code implementations • 2 Dec 2021 • Tran Viet Khoa, Dinh Thai Hoang, Nguyen Linh Trung, Cong T. Nguyen, Tran Thi Thuy Quynh, Diep N. Nguyen, Nguyen Viet Ha, Eryk Dutkiewicz
Challenges in implementation of FL in such systems include unavailability of labeled data and dissimilarity of data features in different IoT networks.
no code implementations • 17 Jun 2021 • Yuris Mulya Saputra, Diep N. Nguyen, Dinh Thai Hoang, Quoc-Viet Pham, Eryk Dutkiewicz, Won-Joo Hwang
In this work, we propose a novel framework to address straggling and privacy issues for federated learning (FL)-based mobile application services, taking into account limited computing/communications resources at mobile users (MUs)/mobile application provider (MAP), privacy cost, the rationality and incentive competition among MUs in contributing data to the MAP.
no code implementations • 7 Mar 2021 • Nguyen Van Huynh, Dinh Thai Hoang, Diep N. Nguyen, Eryk Dutkiewicz
The jointly optimal framework in this article is also applicable to any distributed learning scheme with heterogeneous and uncertain computing nodes.
no code implementations • 15 Feb 2021 • Cong T. Nguyen, Nguyen Van Huynh, Nam H. Chu, Yuris Mulya Saputra, Dinh Thai Hoang, Diep N. Nguyen, Quoc-Viet Pham, Dusit Niyato, Eryk Dutkiewicz, Won-Joo Hwang
Finally, we highlight important challenges, open issues, and future research directions of TL in future wireless networks.
no code implementations • 29 Jan 2021 • Cong T. Nguyen, Dinh Thai Hoang, Diep N. Nguyen, Yong Xiao, Hoang-Anh Pham, Eryk Dutkiewicz, Nguyen Huynh Tuong
Furthermore, the game model can enhance the security and performance of FedChain.
Computer Science and Game Theory Cryptography and Security
no code implementations • 1 Jan 2021 • Yuris Mulya Saputra, Dinh Thai Hoang, Diep N. Nguyen, Le-Nam Tran, Shimin Gong, Eryk Dutkiewicz
Federated learning (FL) can empower Internet-of-Vehicles (IoV) networks by leveraging smart vehicles (SVs) to participate in the learning process with minimum data exchanges and privacy disclosure.
no code implementations • 13 May 2020 • Nguyen Van Huynh, Dinh Thai Hoang, Diep N. Nguyen, Eryk Dutkiewicz
In this paper, we introduce DeepFake, a novel deep reinforcement learning-based deception strategy to deal with reactive jamming attacks.
Face Swapping Networking and Internet Architecture Information Theory Signal Processing Information Theory
no code implementations • 2 May 2020 • Nguyen Van Huynh, Diep N. Nguyen, Dinh Thai Hoang, Eryk Dutkiewicz
To that end, we develop a lightweight yet very effective parallel Q-learning algorithm to quickly obtain the optimal policy by simultaneously learning from various vehicles.
no code implementations • 4 Apr 2020 • Yuris Mulya Saputra, Diep N. Nguyen, Dinh Thai Hoang, Thang Xuan Vu, Eryk Dutkiewicz, Symeon Chatzinotas
In this paper, we propose a novel energy-efficient framework for an electric vehicle (EV) network using a contract theoretic-based economic model to maximize the profits of charging stations (CSs) and improve the social welfare of the network.
Networking and Internet Architecture Signal Processing
no code implementations • 3 Sep 2019 • Yuris Mulya Saputra, Dinh Thai Hoang, Diep N. Nguyen, Eryk Dutkiewicz, Markus Dominik Mueck, Srikathyayani Srikanteswara
Through experimental results, we show that our proposed approaches can improve the accuracy of energy demand prediction up to 24. 63% and decrease communication overhead by 83. 4% compared with other baseline machine learning algorithms.
no code implementations • 8 Apr 2019 • Nguyen Van Huynh, Diep N. Nguyen, Dinh Thai Hoang, Eryk Dutkiewicz
Bringing together the latest advances in neural network architectures and ambient backscattering communications, this work allows wireless nodes to effectively "face" the jammer by first learning its jamming strategy, then adapting the rate or transmitting information right on the jamming signal.
no code implementations • 26 Feb 2019 • Nguyen Van Huynh, Dinh Thai Hoang, Diep N. Nguyen, Eryk Dutkiewicz
This article develops an optimal and fast real-time resource slicing framework that maximizes the long-term return of the network provider while taking into account the uncertainty of resource demand from tenants.
no code implementations • 8 Sep 2018 • Nguyen Van Huynh, Dinh Thai Hoang, Diep N. Nguyen, Eryk Dutkiewicz, Dusit Niyato, Ping Wang
To cope with such incomplete knowledge of the environment, we develop a low-complexity online reinforcement learning algorithm that allows the secondary transmitter to "learn" from its decisions and then attain the optimal policy.
no code implementations • 16 Dec 2017 • Khoi Khac Nguyen, Dinh Thai Hoang, Dusit Niyato, Ping Wang, Diep Nguyen, Eryk Dutkiewicz
With the rapid growth of mobile applications and cloud computing, mobile cloud computing has attracted great interest from both academia and industry.