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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2025年 6卷 4405-4420页

作者： Ryu, Won Jae Lee, Jae-Min Kim, Dong-Seong Kumoh Natl Inst Technol ICT Convergence Res Ctr Gumi 39177 South Korea Kumoh Natl Inst Technol IT Convergence Engn Gumi 39177 South Korea

This paper proposes a Multi-Agent Quantum Deep Reinforcement Learning (MA-QDRL) framework to optimize uplink access in Irregular Repetition Slotted ALOHA with Non-Orthogonal Multiple Access (IRSA-NOMA) systems under practical constraints such as finite frame lengths and fading channels. IRSA enhances reliability by allowing devices to transmit multiple packet replicas across random time slots, while NOMA increases spectral efficiency through power-domain multiplexing with successive interference cancellation (SIC). As a benchmark, Contention Resolution Diversity Slotted ALOHA (CRDSA) improves traditional ALOHA through packet repetition and interference cancellation, maintaining solid performance under moderate network loads;however, its efficiency gradually declines under heavier traffic due to increased collisions and limited adaptability. Conventional multi-agent deep reinforcement learning (MA-CDRL) approaches have been explored to address coordination challenges in such environments. Nevertheless, these models often experience scalability limitations and unstable convergence as the number of agents increases. To overcome these challenges, MA-QDRL integrates variational quantum circuits into agents' policy networks to improve learning efficiency and convergence. Simulation results demonstrate that MA-QDRL reduces packet loss rate by 63.2% compared to classical DRL and by 39.2% compared to CRDSA-NOMA under high network load conditions (G = 1.0), confirming its effectiveness in highly congested IoT environments. In addition, it reduces overall computational cost compared to MA-CDRL. These results highlight the potential of MA-QDRL as a scalable and efficient solution for dynamic multi-agent wireless access in next-generation IoT networks.

关键词： NOMA internet of Things Uplink Fading channels Wireless communication Quantum computing Optimization Convergence Reliability Interference cancellation Quantum neural network (QNN) multi-agent reinforcement learning quantum deep reinforcement learning (QDRL) uplink random access IRSA-NOMA Optimization finite-length frame adaptive repetition control wireless IoT networks

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performance Impact of control Flow Enforcement Technology (CET) 25

Performance Impact of Control Flow Enforcement Technology (C...

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25th conference on Innovation in Clouds, internet and networks (ICIN)

作者： Kucab, Michal Borylo, Piotr Cholda, Piotr AGH Univ Sci & Technol Akamai Technol Poland Krakow Poland AGH Univ Sci & Technol Inst Telecommun Krakow Poland

ISBN: (纸本)9781728186887

The application flow integrity plays a critical role in proper system behavior. Unfortunately, taking over the control of the flow is still difficult to detect. We evaluate the performance of hardware support for the application flow integrity control. A solution is based on platform capabilities and is said to overcome existing limitations met in similar systems. Our results show that overhead is acceptable and with minimal effort, the solution can be successfully used in practical applications.

关键词： CET ROP CFI CFG

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Deep Causal Domain Generalization network for Human Action Recognition in internet of Behaviors 21

Deep Causal Domain Generalization Network for Human Action R...

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21st International conference on networking, Sensing and control, ICNSC 2024

作者： Zhong, Ruirui Feng, Yixiong Hu, Bingtao Zhang, Zhifeng Song, Xiuju Tan, Jianrong Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems Hangzhou China Guizhou University State Key Laboratory of Public Big Data Guiyang China

ISBN: (纸本)9798350365221

The internet of Behaviors (IoB) aims to leverage advanced sensing and computing technologies to understand human behaviors, and human action recognition is an essential task in IoB. However, recognizing human actions across varied and unseen domains remains a challenging problem, largely due to domain shifts that degrade the performance of conventional models. To address the aforementioned issue, a Deep Causal Domain Generalization network (DCDGNet) is designed to enhance the generalizability of the human action recognition model. Specifically, a structured causal model for the data generation of human motion is constructed, where human motion signals are decoupled into action class-related causal factors and domain shift-related non-causal factors. DCDGNet integrates a human domain encoder, causal encoder, domain discriminator, motion reconstruction decoder, and action classifier, contributing to the model's ability to disentangle causal factors from non-causal ones from domain-specific features and achieve domain-invariant representations. Comprehensive experiments confirm that DCDGNet significantly outperforms existing state-of-the-art methods in terms of both accuracy and generalizability. © 2024 IEEE.

关键词： Signal encoding

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Q-Learning Based Adaptive Flow control 5

Q-Learning Based Adaptive Flow Control

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5th International conference on Data-Driven Optimization of Complex systems, DOCS 2023

作者： Zhang, Xiaoping Wang, Yunlan Zhao, Tianhai Hu, Shuai Zhang, Hui School of Software Northwestern Polytechnical University Xi'an China School of Computer Science Northwestern Polytechnical University Xi'an China China Unicom Western Innovation Research Institute R&d Department 1 Xi'an China

ISBN: (纸本)9798350393521

With the rapid development of the internet and the expansion of its application scale, the volatility and complexity of network flow in highly concurrent environments have become increasingly prominent. Traditional static control strategies have been difficult to meet the actual needs. Traditional flow control requires manual parameter setting based on thresholds, which not only requires a large number of parameter settings but also affects system availability. This paper proposes an adaptive flow control algorithm based on q-learning, designs and implements a q-learning model for adaptive flow control, and considers the three elements of state, action and reward, where the state includes CPU utilization, average response time, requests per second, and concurrent threads;actions include flow control decisions, that is pass or reject;rewards represent the feedback results of each decision. The algorithm can use the trained q-learning model to select the best flow control decision based on the current state, so as to realize the adaptive adjustment and control of network flow. In the case of high network load, the algorithm will intelligently adjust the flow control strategy to reduce network congestion and delay, improve network performance and reliability, and solve the problem of system instability caused by high throughput under high concurrency. © 2023 IEEE.

关键词： Flow control

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ImageFed: Practical Privacy Preserving Intrusion Detection System for In-Vehicle CAN Bus Protocol 9

ImageFed: Practical Privacy Preserving Intrusion Detection S...

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9th IEEE International conference on Big Data Security on Cloud (BigDataSecurity) / 9th IEEE International conference on High performance and Smart Computing (IEEE HPSC) / 9th IEEE International conference on Intelligent Data and Security (IEEE IDS)

作者： Taslimasa, Hamideh Dadkhah, Sajjad Pinto Neto, Euclides Carlos Xiong, Pulei Iqbal, Shahrear Ray, Suprio Ghorbani, Ali A. UNB Fac Comp Sci Fredericton NB Canada Natl Res Council Canada Cybersecur Ottawa ON Canada Natl Res Council Fredericton New Brunswick NJ USA

ISBN: (纸本)9798350312935

In vehiclenetworks handle the communication among Electronic control Units (ECUs) and sensors with a serial protocol called controller Area network (CAN), which takes advantage of a broadcast mechanism liar transferring data. However, the lack of authentication and encryption in CAN bus might lead to the vehicle being compromised by an untrusted agent. Meanwhile, AI -based network Intrusion Detection systems (NIDS) introduced in the literature require a centralized cloud server to undertake the resource -consuming task of processing network data, which also occupies the bandwidth while transferring data from vehicles to the remote server. Moreover, many car manufacturers avoid sharing information with the remote server to prevent further exposure of vehicles' private data. We utilize Federated Learning (FL) as a suitable solution to previous challenges, where data is trained locally on the vehicle side, and partially trained models are sent to the server. Nevertheless, private training inside the vehicle requires low memory utilization while achieving low latency and high performance compared to Centralized Learning (CL) approaches. In this study, we propose a practical privacy-preserving IDS (ImageFed) approach by employing federated Convolutional Neural network (CNN). To evaluate the robustness of ImageFed in real -world applications, we investigate two scenarios that may cause performance degradation in FL, such as non-independent and identically distributed (non-iid) clients and scarcity of training data during the process. The resulting analysis proves the robustness of ImageFed while delivering an average 99.54% fl -score, 99.87% accuracy, and low detection latency.

关键词： Terms internet of Vehicles (IoV) Security Intrusion Detection systems (IDS) Federated Learning (FL) I)eep Learning (DL) Convolutional Neural networks (CNN)

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Energy Optimization for Building Energy Management with Thermal Storage: A Multi-Agent Deep Reinforcement Learning Approach 43

Energy Optimization for Building Energy Management with Ther...

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43rd Chinese control conference (CCC)

作者： Zhu, Shifeng Lu, Wei Feng, Yi Sun, Changhai Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China Dalian Univ Technol Coll Elect Engn Dalian 116024 Peoples R China

ISBN: (纸本)9798350366907;9789887581581

Building Energy Management (BEM) with Thermal Energy Storage (TES) poses significant challenges due to the intricate coordination required among components such as Power-to-Heat (P2H) converters, TES units, and zone temperature controllers. In this paper, we propose a novel multi-agent Deep Reinforcement Learning (DRL) method for BEM, capable of optimizing the energy efficiency and flexibility of Building Energy systems (BESs) integrated with TES without requiring manual efforts to set up a control-oriented model. Our method-distributed optimized NNPC via DRL-consists of two parts: i) a Neural network Predictive control (NNPC) for zone temperature regulation, leveraging an attention-based artificial neural network for forecasting operational conditions and a Dynamic Programming (DP) algorithm to jointly optimize both comfort levels and energy efficiency;and ii) a distributed optimization strategy realized through a coordinating policy that facilitates the collaboration between conventional on/off and NNPC agents/controllers. The efficacy of our method is confirmed through simulations established based on a real-world BES that integrates a TES unit. We compare our method with three alternatives: a conventional on/off control, an optimized on/off control, and an optimized Naive NNPC, with the latter two also incorporating our distributed optimization strategy. Specifically, our method realizes approximately 7% reduction in energy consumption compared to the conventional on-off method while maintaining equivalent comfort levels. Furthermore, it attains parity with the performance of the optimized Naive NNPC without requiring manual tuning, thereby illustrating its adaptability and ease of implementation. Collectively, these findings underscore our method's superiority across metrics of energy efficiency, comfort and applicability, demonstrating the profound potential of DRL in BEM, particularly when TES is involved.

关键词： Building Energy Management Thermal Energy Storage Operational Optimization Deep Reinforcement Learning Neural network Predictive control

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Post-Quantum Cryptography (PQC) network Instrument: Measuring PQC Adoption Rates and Identifying Migration Pathways 5

Post-Quantum Cryptography (PQC) Network Instrument: Measurin...

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2024 International conference on Quantum Computing and Engineering

作者： Sowa, Jakub Hoang, Bach Yeluru, Advaith Qie, Steven Nikolich, Anita Iyer, Ravishankar Cao, Phuong Natl Ctr Supercomputing Applicat Urbana IL 61801 USA Univ Illinois Champaign IL USA

ISBN: (纸本)9798331541378

The problem of adopting quantum-resistant cryptographic network protocols or post-quantum cryptography (PQC) is critically important to democratizing quantum computing. The problem is urgent because practical quantum computers will break classical encryption in the next few decades. Past encrypted data has already been collected and can be decrypted in the near future. The main challenges of adopting post-quantum cryptography lie in algorithmic complexity and hardware/software/network implementation. The grand question of how existing cyberinfrastructure will support post-quantum cryptography remains unanswered. This paper describes: i) the design of a novel Post-Quantum Cryptography (PQC) network instrument placed at the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign and a part of the FABRIC testbed;ii) the latest results on PQC adoption rate across a wide spectrum of network protocols (Secure Shell - SSH, Transport Layer Security - TLS, etc.);iii) the current state of PQC implementation in key scientific applications (e.g., OpenSSH or SciTokens);iv) the challenges of being quantum-resistant;and v) discussion of potential novel attacks. This is the first large-scale measurement of PQC adoption at national-scale supercomputing centers and FABRIC testbeds. Our results show that only OpenSSH and Google Chrome have successfully implemented PQC and achieved an initial adoption rate of 0.029% (6,044 out of 20,556,816) for OpenSSH connections at NCSA coming from major internet Service Providers or Autonomous systems (ASes) such as OARNET, GTT, Google FiberWebpass (U.S.) and Uppsala Lans Landsting (Sweden), with an overall increasing adoption rate year-over-year for 2023-2024. Our analyses identify pathways to migrate current applications to be quantum-resistant.

关键词： adoption rate algorithmic complexity cryptography cyberinfrastructure distributed encrypted data FABRIC Google Chrome migration NCSA networking NIST novel attacks OpenSSH performance Phuong Phuong Cao post-quantum cryptography PQC PQC network instrument quantum computing quantum-resistant quantum-resistant applications scientific applications SSH systems testbed TLS

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PINet: Towards Effective and Efficient Industrial control Protocol Identification

PINet: Towards Effective and Efficient Industrial Control Pr...

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2023 International conference on Artificial Intelligence of Things and systems, AIoTSys 2023

作者： Jin, Jianxin Sha, Wanli Zhao, Chaoyang Wang, Zhan Zhong, Yaoyi Zhou, Junliang Zhejiang Zheneng Jiahua Power Co. Ltd. China Zhejiang Energy Digital Technology Co. Ltd. China Zhejiang Petroleum Comprehensive Energy Sales Co. Ltd. China

ISBN: (纸本)9798350312270

Accurate identification of industrial control protocols plays a pivotal role in ensuring the secure and reliable operation of Industrial internet of Things (iioT) systems. In this paper, we propose, PINet, a novel model that employs a combination of a Gated Recurrent Unit (GRU) network and a multi-head attention mechanism for industrial control protocol identification. PINet effectively captures the underlying patterns within the input sequence by utilizing the hidden state representations obtained from the GRU network. Furthermore, the multi-head attention mechanism facilitates the extraction of crucial information from these hidden states. Ultimately, a classifier linear layer is employed to map the extracted feature vectors onto the probability distribution of protocol categories. Experimental evaluations demonstrate that PINet achieves outstanding performance and accuracy in classifying industrial control protocols, showcasing its effectiveness and reliability in practical applications. © 2023 IEEE.

关键词： Probability distributions

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Research on the Application of Zero Trust Framework in the Design of Power System network Architecture 3

Research on the Application of Zero Trust Framework in the D...

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3rd International conference on High performance Computing and Communication Engineering, HPCCE 2023

作者： Yang, Chunyan Han, Songming Bin, DongMei Ling, Ying Fu, Hua Electric Power Research Institute of Guangxi Power Grid Co. Ltd. Guangxi Nanning530023 China Nanning Power Supply Bureau of Guangxi Power Grid Co. Ltd. Guangxi Nanning530023 China

ISBN: (数字)9781510674653

ISBN: (纸本)9781510674646

As the power system undergoes continuous digitization and networking, the associated network security challenges have become increasingly prominent. Addressing these issues, the zero trust framework emerges as a cutting-edge security concept that underscores sustained vigilance toward users, devices, and applications within the network environment. Its core principle involves minimizing the scope of trust granted to the smallest possible extent. This study delves into the application of the zero trust framework in crafting the architecture of power system networks, with the primary objective of enhancing network security and thwarting potential threats. The article commences by scrutinizing the network security requirements of internet applications and subsequently proposes a zero trust architecture as a security solution. The construction of a zero trust-based smart grid security protection architecture follows, encompassing elements such as terminal trusted perception agents, multi-source data aggregation platforms, intelligent trust evaluation platforms, dynamic access control platforms, and trusted access agents. Finally, the efficacy of the zero trust framework in power system network architecture is validated through case studies and simulation experiments. The research findings highlight a significant enhancement in the network security of the power system with the incorporation of the zero trust framework. This, in turn, establishes an effective security mechanism conducive to the digital transformation of future power systems. In summary, the study contributes a novel security approach and technical solution for the design of power system network architecture, offering valuable insights and reference for the development of the power system network security field. © 2024 SPIE.

关键词： network architecture

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Measurement of Cloud-based Game Streaming System Response to Competing TCP Cubic or TCP BBR Flows 22

Measurement of Cloud-based Game Streaming System Response to...

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22nd ACM internet Measurement conference (IMC)

作者： Xu, Xiaokun Claypool, Mark Worcester Polytech Inst Worcester MA 01609 USA

ISBN: (纸本)9781450392594

Cloud-based game streaming is emerging as a convenient way to play games when clients have a good network connection. However, high-quality game streams need high bitrates and low latencies, a challenge when competing for network capacity with other flows. While some network aspects of cloud-based game streaming have been studied, missing are comparative performance and congestion responses to competing TCP flows. This paper presents results from experiments that measure how three popular commercial cloud-based game streaming systems - Google Stadia, NVidia GeForce Now, and Amazon Luna - respond and then recover to TCP Cubic and TCP BBR flows on a congested network link. Analysis of bitrates, loss rates and round-trip times show the three systems have markedly different responses to the arrival and departure of competing network traffic.

关键词： Transmission control protocol

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