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Differential Fault Attack of Lightweight Cipher GIFT Based on Byte Model

IEEE INTERNET OF THINGS JOURNAL 2025年第1期12卷 435-444页

作者： Gao, Yuan Zhang, Zhongya Zhang, Zhiyong Henan Univ Sci & Technol Informat Engn Coll Luoyang 471023 Peoples R China

GIFT, as a lightweight block cipher algorithm, is mainly suitable for resource-constrained environments, such as the Internet of Things, to achieve efficient cryptographic communication. For the diffusion characteristics of round function of the GIFT algorithm, this article proposes two byte-based differential fault attack models. Theoretically, the first and second models require 53.44 and 12.42 byte faults to recover the master key. Experimental results show that the first and second models require about 79 and 16 byte faults, respectively, to recover the master key. The models proposed in this article have made significant breakthroughs in terms of both the attack range and the number of required faults, which provide important guidance for algorithmic security research and the design of fault-tolerant mechanisms for IoT devices.

关键词： Cryptography Internet of Things Ciphers Computational modeling clustering algorithms Technological innovation Manufacturing Block cipher differential analysis differential fault attack GIFT lightweight cipher

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Dual-Path Beam Tracking for Service Continuity of Ultra-Reliable and Low-Latency Communications

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IEEE TRANSACTIONS ON COMMUNICATIONS 2025年第1期73卷 524-539页

作者： Wang, Rui She, Changyang Li, Chunhui Li, Yonghui Vucetic, Branka Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia Harbin Inst Technol Shenzhen Sch Elect & Informat Engn Shenzhen 518055 Peoples R China Ericsson Res Yokohama 2200041 Japan

Multi-antenna millimeter-wave (mmWave) communication systems have become a promising approach to improve throughput. However, mmWave narrow beams are susceptible to blockages, making it difficult to guarantee continuous services for ultra-reliable and low-latency communications (URLLC). To address this issue, we propose a novel dual-path beam tracking framework and develop a Recurrent Neural Network-based Constrained Deep Reinforcement Learning (RCDRL) algorithm to optimize the beam search sets of the beam tracking algorithm. The objective is to minimize the total time and frequency resources allocated for beam sweeping, beam tracking, and data transmission, subject to the constraint on the service interruption probability of URLLC. A pre-training method is developed to improve the initial performance and stability of the RCDRL algorithm. Comprehensive evaluation results indicate that the proposed approach outperforms other baseline methods in terms of the tradeoff between service interruption probability and resource utilization efficiency. Specifically, the RCDRL algorithm reduces the service interruption probability by three orders of magnitude compared with a standardized single-beam tracking method at the cost of sacrificing the resource utilization efficiency by 14.6%. In addition, our policy achieves lower service interruption probability and higher resource utilization efficiency compared with an existing dual-beam tracking algorithm.

关键词： Millimeter wave communication clustering algorithms Ultra reliable low latency communication Signal to noise ratio Resource management Training Prediction algorithms Beam tracking ultra-reliable and low-latency communications deep reinforcement learning mobile communication network millimeter-wave communications

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A Flexible Multi-Rate Adaptive Blind Equalization Scheme Based on Constellation Probability Shaping in Coherent Optical Fiber Communication Systems

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2025年第7期43卷 3078-3089页

作者： Yuan, Xinyu Zhang, Qi Gao, Ran Yao, Haipeng Zhao, Qihan Tian, Feng Wang, Fu Xin, Gang Tian, Qinghua Wang, Yongjun Beijing Univ Posts & Telecommun Sch Elect Engn State Key Lab Informat Photon & Opt Commun Beijing 100876 Peoples R China Beijing Inst Technol Beijing 100081 Peoples R China Beijing Univ Posts & Telecommun Beijing Key Lab Space Round Interconnect & Converg Beijing 100876 Peoples R China Changzhou Jingxin New Generat Informat Technol Res Changzhou 213161 Peoples R China

A flexible multi-rate adaptive blind equalization scheme based on probabilistic shaping (PS) in coherent optical fiber communication systems is proposed, addressing the performance degradation of clustering algorithms when performing nonlinear blind equalization for high-order modulation signals in PS systems. The scheme analyzes received signals distribution using the discrete Gaussian fitting theory and combines it with a weighted clustering algorithm to implement the Gaussian-weighted decision equalization (GWDE) method, enabling adaptive blind equalization for PS signals. Moreover, the scheme utilizes the probability distribution characteristics of the equalization process to implement the shaping degree estimation (SDE), enabling accurate quantification of the signal shaping degree. The proposed scheme has been experimentally and numerically validated in a 64QAM coherent optical fiber communication system. The results show that for $\nu = 0.05$ PS-64QAM signals, the proposed scheme improves tolerance to nonlinear effects caused by launched optical power (LOP). Compared to the unequalized signal, tolerance to these effects increases by over 1 dBm;relative to the K-means equalized signal, tolerance to LOP nonlinear effects improves by approximately 0.7 dBm. Additionally, the scheme reduces computational complexity by 12% compared to K-means. The proposed scheme also achieves 100% accuracy in estimating the degree of signal shaping, given the prerequisite of ensuring reliable communication. This approach holds significant potential for the dynamic rate transmission of diverse fine-grained services in future optical fiber communication systems.

关键词： Blind equalizers Fitting Probabilistic logic Optical fibers Optical fiber networks Adaptive equalizers Accuracy Optical transmitters Estimation clustering algorithms Blind equalization probabilistic shaping discrete Gaussian fitting coherent optical fiber communication

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Field-Based Security Testing of SDN Configuration Updates

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IEEE TRANSACTIONS ON RELIABILITY 2025年

作者： Malik, Jahanzaib Pastore, Fabrizio Univ Luxembourg SnT Ctr L-4365 Esch Sur Alzette Luxembourg

Software-defined systems revolutionized the management of hardware devices but introduced quality assurance challenges that remain to be tackled. For example, software defined networks (SDNs) became a key technology for the prompt reconfigurations of network services in many sectors including telecommunications, data centers, financial services, cloud providers, and manufacturing industry. Unfortunately, reconfigurations may lead to mistakes that compromise the dependability of the provided services. In this article, we focus on the reconfigurations of network services in the satellite communication sector, and target security requirements, which are often hard to verify;for example, although connectivity may function properly, confidentiality may be broken by packets forwarded to a wrong destination. We propose an approach for FIeld-based Security Testing of SDN Configurations Updates (FISTS). First, it probes the network before and after configuration updates. Then, using the collected data, it relies on unsupervised machine learning algorithms to prioritize the inspection of suspicious node responses, after identifying the network nodes that likely match across the two configurations. Our empirical evaluation has been conducted with network data from simulated and real SDN configuration updates for our industry partner, a world-leading satellite operator. Our results show that, when combined with K-Nearest Neighbor, FISTS leads to best results (up to 0.95 precision and 1.00 recall). Further, we demonstrated its scalability.

关键词： clustering algorithms Testing Security Software Nearest neighbor methods Anomaly detection Partitioning algorithms Inspection Software algorithms Machine learning algorithms Field-based testing software defined network (SDN) security testing upgrades testing

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Usability and Perceived Efficiency of an Adaptive Route Optimization Solution for Commercial Vehicles

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IEEE ACCESS 2025年 13卷 61091-61108页

作者： Anghelache, Florian Goga, Nicolae Marian, Constantin Viorel Alexandru Mitrea, Dan Pavaloiu, Ionel-Bujorel iSYS Profess Res & Dev Dept Bucharest 020312 Romania Natl Univ Sci & Technol POLITEHN Bucharest Fac Engn Foreign Languages FILS Dept Engn Foreign Languages Bucharest 060042 Romania Univ Groningen Mol Dynam Grp NL-9747 AG Groningen Netherlands

This paper details the outcome of a European scientific research and development project, funded to prototype a commercial solution for vehicle fleets. While current route optimization solutions have data sources such as real-time road traffic and weather conditions, our solution was developed to offer companies a proposed plan with adapted optimized routes, considering critical operational requirements, local conditions, and drivers' driving experience. The iRoute solution consists of a module for receiving vehicle positions from GPS devices, an integration layer for end-customer applications, a route optimization engine, and applications for web and mobile users. Unlike other approaches, this innovative solution leverages historical, well-known, and familiar route data to enhance, refine, and adapt the optimized operational routes. This way, the drivers benefit from a more effective daily plan to follow based on business requirements such as locations, quantities, time, and distance. At the end of the day, adding these adjustments results in achieving up to a 20% improvement in the accuracy of executed routes, in terms of distance and time, compared to the planned route, outperforming standard optimization algorithms. This allows business owners to rely more confidently on the optimization results, providing a safer itinerary for their drivers. Also, due to lower incident rates on these known and preferred itineraries, the overall business will benefit from increased vehicle fleet availability and fewer days in service, with lower maintenance costs. The paper evaluates the usability and perceived efficiency value of the proposed solution by statistically analyzing feedback from dispatchers and business owners.

关键词： Vehicles Costs clustering algorithms Business Routing Optimization Companies Usability Software algorithms Software Adaptive algorithms adaptive systems path planning roadmaps (technology planning) road traffic control vehicle routing

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On the Distribution of Subnetwork Size With Virtual-Cell-Based Optimal Decomposition for Next-Generation Wireless Networks

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第4期24卷 3073-3087页

作者： Du, Liang Wang, Junyuan Dai, Lin Bai, Bo City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China Tongji Univ Shanghai Inst Intelligent Sci & Technol Coll Elect & Informat Engn Shanghai 201804 Peoples R China Tongji Univ Inst Adv Study Shanghai 201804 Peoples R China Huawei Technol Co Ltd Cent Res Inst Theory Lab Labs 2012 Hong Kong Peoples R China

For large-scale wireless networks, the optimal network decomposition has been proposed from a graph theoretical perspective to maximize the number of decomposed subnetworks with an interference constraint. When each user is associated with multiple base-stations (BSs) to form its virtual cell, however, the optimal network decomposition may lead to highly unbalanced subnetwork sizes if the key system parameters are not properly set. In this paper, an analytical framework is proposed to characterize the distribution of subnetwork size in terms of the number of users in a randomly chosen subnetwork for virtual-cell-based optimal decomposition. The analysis reveals that the distribution of subnetwork size is crucially determined by the virtual cell size, i.e., the number of BSs associated with each user, and the ratio of the number of users to the number of BSs, both of which should be carefully tuned to produce balanced subnetworks.

关键词： Bipartite graph Interference Matrix decomposition Wireless networks clustering algorithms Complexity theory Accuracy Vectors Urban areas Unsupervised learning Virtual cell optimal network decomposition subnetwork size active intersection graph random bipartite graph component size

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Guaranteeing Data Privacy in Federated Unlearning With Dynamic User Participation

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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2025年第3期22卷 2072-2085页

作者： Liu, Ziyao Jiang, Yu Jiang, Weifeng Guo, Jiale Zhao, Jun Lam, Kwok-Yan Nanyang Technol Univ Singapore 639798 Singapore

Federated Unlearning (FU) is gaining prominence for its capability to eliminate influences of specific users' data from trained global Federated Learning (FL) models. A straightforward FU method involves removing the unlearned user-specified data and subsequently obtaining a new global FL model from scratch with all remaining user data, a process that unfortunately leads to considerable overhead. To enhance unlearning efficiency, a widely adopted strategy employs clustering, dividing FL users into clusters, with each cluster maintaining its own FL model. The final inference is then determined by aggregating the majority vote from the inferences of these sub-models. This method confines unlearning processes to individual clusters for removing the training data of a particular user, thereby enhancing unlearning efficiency by eliminating the need for participation from all remaining user data. However, current clustering-based FU schemes mainly concentrate on refining clustering to boost unlearning efficiency but without addressing the issue of the potential information leakage from FL users' gradients, a privacy concern that has been extensively studied. Typically, integrating secure aggregation (SecAgg) schemes within each cluster can facilitate a privacy-preserving FU. Nevertheless, crafting a clustering methodology that seamlessly incorporates SecAgg schemes is challenging, particularly in scenarios involving adversarial users and dynamic users. In this connection, we systematically explore the integration of SecAgg protocols within the most widely used federated unlearning scheme, which is based on clustering, to establish a privacy-preserving FU framework, aimed at ensuring privacy while effectively managing dynamic user participation. Comprehensive theoretical assessments and experimental results show that our proposed scheme achieves comparable unlearning effectiveness, alongside offering improved privacy protection and resilience in the face of varying user pa

关键词： Protocols Privacy Servers Data models Computational modeling Training Federated learning Information leakage Computational efficiency clustering algorithms AI safety digital trust federated unlearning privacy preservation user dynamics

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Adaptive Beam Tracking in 5G/6G mmWave Networks: A Clustered Federated Learning Approach

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IEEE ACCESS 2025年 13卷 70705-70720页

作者： Ali, Amjad Koucheryavy, Yevgeni HSE Univ Telecommun Res & Dev Inst MIEM Moscow 123458 Russia

Millimeter wave (mmWave, 30-100 GHz) communication is essential for meeting the high data throughput demands of 5G/6G networks. However, mmWave signals are highly susceptible to attenuation and blockage, necessitating directional beamforming antennas and efficient beam tracking algorithms. Traditional machine learning-based approaches, such as centralized learning (CL) and federated learning (FL), face significant challenges. While CL achieves fast convergence, it suffers from high computational costs and privacy concerns. Conversely, FL addresses these issues by enabling distributed model training but struggles with slow convergence and suboptimal accuracy due to data heterogeneity. To overcome these limitations, this paper introduces a novel clustered federated learning (CFL) framework for beam tracking. CFL leverages the benefits of FL while grouping users with similar data distributions, enabling the training of a single model per cluster. This approach reduces communication overhead, accelerates training, and improves accuracy. We analyze key attributes influencing user clustering and their impact on learning efficiency. Numerical results demonstrate that CFL significantly outperforms traditional CL and FL methods, achieving an 18% accuracy improvement over FL (specifically, the FedAvg algorithm) and an 11% enhancement over CL. These findings highlight the potential of CFL for enhancing beam tracking in mmWave systems, offering a more adaptive and privacy-preserving solution for future wireless networks.

关键词： Millimeter wave communication Training Accuracy Data models Radar tracking Adaptation models Tracking Federated learning Convergence clustering algorithms Millimeter wave centralized learning federated learning clustered federated learning beam tracking ray-tracing channel prediction FedAvg

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Gaussian Mixture Model-Based Vector Approach to Real-Time Three-Dimensional Path Planning in Cluttered Environment

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IEEE ACCESS 2025年 13卷 8077-8091页

作者： Tullu, Abera Cho, Yunsang Ko, Sangho Korea Aerosp Univ Dept Smart Air Mobil Goyang Si 10540 Gyeonggi Do South Korea

This work presents an obstacle-free three-dimensional (3D) path planning algorithm for unmanned aerial vehicles (UAV) navigating in cluttered environments. Gaussian mixture model (GMM), a class of unsupervised machine learning, is employed for environment perception based on a proposed vector approach to obstacle-free path planning. GMM circumscribes an obstacle by an ellipsoidal surface defined by its eigenvectors and eigenvalues. The eigenstructure specifies a region occupied by the obstacle so that the path planner re-routes the path whenever the UAV comes closer to this region. The proposed path planner is verified to bypass the challenges and limitations of computationally lightweight path planners such as artificial potential fields. The path planner is also compared with rapidly exploring random tree and shows better performance in optimal path and low computational time. The performance of the proposed path planner is validated in a simulated environment filled with obstacles of various sizes, shapes, and orientations.

关键词： Autonomous aerial vehicles Path planning Navigation Ellipsoids Three-dimensional displays Vectors Shape Gaussian distribution Covariance matrices clustering algorithms Expectation maximization Gaussian mixture model obstacle avoidance path planning unmanned aerial vehicles

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Classified Identification and Estimation of Behind-the-Meter Distributed Photovoltaic Panels Using High-Resolution Aerial Imagery

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2025年第1期61卷 1604-1612页

作者： Li, Kangping Gong, Mingkai Li, Zhenghui Huang, Chunyi Shanghai Jiao Tong Univ Coll Smart Energy Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Shanghai Noncarbon Energy Convers & Utilizat Inst Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Minist Educ Key Lab Control Power Transmiss & Convers Shanghai 200240 Peoples R China

The continuously increasing penetration of behind-the-meter distributed photovoltaics (PV) proposes significant challenges to the operation of distributed network. Identifying the distributed PV using high-resolution aerial image is a promising and low-cost way to enhance the visibility of distributed PV. Existing studies typically establish a unified identification model, which cannot achieve satisfactory identification performance for different types of distributed PV in practice. To this end, this paper proposes a classified identification and estimation method to accurately acquire the location and size of the installed PV panels within a wide area. Firstly, K-means algorithm is adopted to cluster PV panel images with different size and installation scenarios. For each cluster, U-net is then used to build identification models where focal loss is used as the loss function for better identification of tiny-size panels. Secondly, density-based clustering algorithms and right-angle polygon fit algorithm are employed to optimize the identification results. Finally, the size of PV panels is estimated based on the segmentation masks. The effectiveness of the proposed method has been validated on a real-world aerial imagery dataset.

关键词： Image color analysis Feature extraction Image segmentation Shape Histograms clustering algorithms Distributed photovoltaics behind-the-meter deep learning clustering aerial imagery aerial imagery

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