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An Intelligent Framework for Cluster-Based Side-Channel Analysis on Public-Key Cryptosystems

IEEE INTERNET OF THINGS JOURNAL 2025年第2期12卷 1962-1973页

作者： Wei, Congming He, Shulin Wang, An Sun, Shaofei Ding, Yaoling Zhang, Jingqi Zhu, Liehuang Beijing Inst Technol Sch Cyberspace Sci & Technol Beijing 100081 Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China Chinese Acad Sci Inst Informat Engn State Key Lab Informat Secur Beijing 100093 Peoples R China Beijing Inst Technol Sch Integrated Circuits & Elect Beijing 100081 Peoples R China

Classical cluster-based side-channel analysis (SCA) uses clustering algorithms to analyze power traces and often, principal component analysis to reduce the dimension of data, resulting in that clustering may not deal well with high-dimensional traces, such as cryptographic algorithm implementations with countermeasures. In this article, we propose an intelligent framework for cluster-based SCA, which includes three steps of clustering, classification and correction, for processing large high-dimensional data. By combining unsupervised clustering and supervised deep learning techniques, the framework succeeds in mining the data for additional in-depth information. In addition, unlike traditional cluster-based SCA, our approach focuses on deep learning and deliberately avoids over-reliance on cluster labels during classification. And metrics for correction are adopted to achieve a high level of reliability in key recovery. Experiments on the RSA smart card based on Montgomery ladder implementation and FPGA-based ECC with random delay demonstrate that our framework can significantly improve the success rate with strong robustness.

关键词： clustering algorithms Deep learning Noise Convolutional neural networks Classification algorithms Cryptography Computational modeling Cluster deep learning public-key cryptosystems side-channel analysis (SCA)

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Cooperative Path Planning of Multiple Unmanned Aerial Vehicles Using Cylinder Vector Particle Swarm Optimization With Gene Targeting

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IEEE SENSORS JOURNAL 2025年第5期25卷 8470-8480页

作者： Huang, Chen Ma, Hongjiang Zhou, Xiangbing Deng, Wu Shenyang Aerosp Univ Coll Civil Aviat Shenyang 110136 Peoples R China Yibin Univ Sch Big Data & Artificial Intelligence Yibin 644000 Peoples R China Sichuan Tourism Univ Sch Informat & Engn Chengdu 610100 Peoples R China Civil Aviat Univ China Coll Elect Informat & Automat Tianjin 300300 Peoples R China

Cooperative route planning is crucial to ensure safe and effective flight of unmanned aerial vehicle (UAV) formation. Complicated scenarios, however, put forward new challenges for path planning of multiple UAVs;therefore, a novel cylindrical coordinate particle swarm optimization (PSO) planner with gene targeting (GTCPSO) is presented to obtain feasible and accurate flight routes for multiple UAVs. In the GTCPSO planner, to satisfy UAV dynamic characteristics, the cylindrical coordinate is exploited to represent the flight route;the PSO algorithm with cylindrical coordinates can quickly find a feasible trajectory in a complex environment. Meanwhile, by incorporating gene targeting (GT) operation into improved cylindrical coordinate PSO, the bottleneck waypoint in the global best trajectory is found by waypoint detection, and then the detected bottleneck waypoint is modified by the adjustment operation based on two-phase the updating strategy. A series of experiments based on real map examples on multi-sensors are designed to test the search capability of this GTCPSO planner. The simulation results indicate that the proposed planner GTCPSO has the potential to handle multi-UAV cooperative planning problems under complex constraints.

关键词： Autonomous aerial vehicles Costs Vectors Sensors Radar Planning Heuristic algorithms Search problems clustering algorithms Trajectory Cylinder vector gene targeting multiple unmanned aerial vehicles (UAVs) multisensors particle swarm optimization (PSO) path planning

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A Novel Color Difference-Based Method for Palette Extraction and Evaluation Using Images of Birds

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IEEE ACCESS 2025年 13卷 52270-52283页

作者： Kosesoy, Melike Bektas Yilmaz, Seckin Bursa Tech Univ Grad Sch Dept Comp Engn TR-16310 Yildirim Bursa Turkiye Kocaeli Univ Fac Engn Dept Software Engn TR-41001 Izmit Kocaeli Turkiye Bursa Tech Univ Fac Engn & Nat Sci Dept Comp Engn TR-16310 Yildirim Bursa Turkiye

Color palettes are important sources of inspiration for designers. In most coloring studies, designers and interior architects use ready-made color palettes that are known to be harmonious and popular with clients. In this study, a new color palette extraction and evaluation method is proposed that differs from existing methods because it accelerates designers' product coloring processes by identifying the color-area and color-neighborhood relationship. Furthermore, attractive color palettes will be identified by focusing on living creatures, primarily colorful birds. For this purpose, a new data set of images of birds was created. In the literature, the success of color palettes is usually evaluated through questionnaires, while measurements are made without relying on numerical metrics. In this study and, for the first time, a new metric based on CIEDE2000 and the Hungarian algorithm to measure the accuracy of color palettes is demonstrated. In the proposed method birds are first segmented from background using Mask R-CNN and PointRend models. Then, Fuzzy c-means (FCM), Gaussian Mixture Model (GMM), k-means, Mean Shift, and mini batch k-means algorithms were used to extract the color palette of each bird image. Color reduction based on the CIEDE2000 was performed on the color palettes using various threshold values, resulting in the final color palettes. In the last step, color-area and color-neighborhood relationships are shown through graph-based color palettes. Experimental results showed that the PointRend method, with a 93.92% success rate, produced the most successful results. However, k-means and GMM methods were more successful in extracting the colors of birds. The evaluation of the palettes was performed with the proposed numerical metric rather than questionnaires, resulting in more robust and repeatable findings. Furthermore, the proposed graph-based color palette results in considerably simplified coloring process by reducing the computational complex

关键词： Image color analysis Birds clustering algorithms Accuracy Color Image segmentation Feature extraction Generative adversarial networks Gaussian mixture model Data mining CIEDE2000 color difference color palette extraction clustering algorithms Hungarian algorithm

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Resilient Fusion of LMB Densities Based on Medoids

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IEEE SENSORS JOURNAL 2025年第6期25卷 10370-10379页

作者： Zhou, Yao Gao, Lin Li, Gaiyou Wei, Ping Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

This article deals with the problem of resilient fusion of labeled multi-Bernoulli (LMB) densities, which arises in the situation that the sensor network (SN) undergoes abnormal behaviors like malicious attacks, resulting in the change of transmitted data from each sensor node. Compared to fusion algorithms based on perfect SN conditions, a detection procedure should be deployed before performing fusion so as to exclude abnormal data. To this end, we propose to decompose the LMB densities as the union of Bernoulli components (BCs), and then the medoids of BCs are exploited to form the fused LMB density. Besides, a new density-based spatial clustering of applications with noise (DBSCAN)-based label-matching algorithm is proposed. The performance of the proposed algorithm is verified via simulations.

关键词： Sensors Sensor fusion clustering algorithms Target tracking Radio frequency Filtering algorithms Noise Matched filters Information theory Filtering theory Labeled multi-Bernoulli (LMB) medoid multiobject fusion random finite set resilient algorithm

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Handover-Free Multi-Connectivity Mobility Management for Downlink FD-RAN: A Hierarchical DRL-Based Approach

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2025年第2期11卷 1281-1296页

作者： Zhang, Tianqi Xue, Jianzhe Xu, Yunting Jiao, Luofang Chen, Jiacheng Zhou, Haibo Zhao, Lian Nanjing Univ Sch Elect Sci & Engn Nanjing 210023 Peoples R China Peng Cheng Lab Dept Strateg & Adv Interdisciplinary Res Shenzhen 518066 Peoples R China Toronto Metropolitan Univ Dept Elect & Comp Engn Toronto ON M5B 2K3 Canada

Seamless connectivity and on-demand service provision are considered as the fundamental capabilities in next-generation mobile networks (6G). However, current configuration of single base station (BS) connection and increasingly denser BS deployment pose great challenges for mobile user equipment (UE), due to the frequent handover and limited communication serving capacity. To this end, we investigate the handover-free multi-connectivity mobility management problem in downlink over a novel 6G architecture, namely fully-decoupled radio access network (FD-RAN). Particularly, we formulate the problem as a two-layer task involving UE-BS association and link power control, whose objective is to minimize the long-term absolute difference between UE's serving rate and rate demand. We propose a hierarchical deep reinforcement learning (HDRL)-based scheme to decompose the original problem into two subproblems for efficient resolution. Specifically, a double deep Q-network (DDQN) algorithm is employed to update the multi-connectivity BS cooperation set for each UE at the first layer of HDRL. Then at the second layer, we design a transformer-assisted soft actor-critic (TSAC) algorithm to jointly determine transmission power for all links associated with each UE. Extensive simulations validate the effectiveness of proposed scheme over benchmarks, which is capable of providing seamless connectivity and fine-grained on-demand service for mobile UEs.

关键词： Handover 5G mobile communication Downlink Heuristic algorithms clustering algorithms Resource management 6G mobile communication FD-RAN multi-connectivity mobility management power control hierarchical deep reinforcement learning

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Interpretability-Oriented Adjustment of K-Means: A Multiple-Objective Particle Swarm Optimization Framework

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IEEE ACCESS 2025年 13卷 68084-68096页

作者： Chen, Liang Sun, Leming Zhong, Caiming Ningbo Univ Coll Sci & Technol Cixi 315000 Peoples R China

clustering is an unsupervised machine learning technique used to partition unlabeled data into different groups. However, traditional clustering methods only provide a set of results without any explanations. While numerous methods in the literature attempt to explain clustering results, the explanation rules provided by these models are fixed and sacrificing clustering quality for the sake of making the rules easier to understand. To address this, this paper proposes a feature selection model to balance the interpretability and clustering quality. The model consists of two stages. In the first stage, the concept of explanatory radius is defined, and the explanatory radius of each cluster in the initial clustering results is calculated. In the second stage, a feature subspace is selected, and the dataset is re-clustered in this subspace using a proposed formula to obtain a new clustering result. Three objective functions are designed to evaluate the performance of the new clustering results in terms of cluster quality and interpretability. Subsequently, the model utilizes a multi-objective optimization framework to optimize the three objective functions in the solution space formed by different feature subspaces, resulting in a set of Pareto solutions. Each solution in the Pareto set represents a trade-off between clustering quality and interpretability. Finally, the contribution of features during the generation process of each cluster is used to represent the interpretability of each cluster's result. The proposed method is validated on both artificial datasets and real-world datasets, and comparative analyses are conducted with the latest explainable clustering algorithms in the literature. The analysis results demonstrate the effectiveness of the proposed method.

关键词： clustering algorithms Optimization Linear programming Machine learning algorithms Decision trees Prediction algorithms Particle swarm optimization Vectors Prototypes Feature extraction Feature selection interpretability K-means clustering

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Deep Spiking Neural Networks Driven by Adaptive Interval Membrane Potential for Temporal Credit Assignment Problem

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年第1期9卷 717-728页

作者： Jiang, Jiaqiang Ding, Haohui Wang, Haixia Yan, Rui Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Peoples R China

One of the core challenges in biological and machine learning is how neural networks can discover the predictive features hidden in distracting streams of unrelated sensory activity via delayed feedback, commonly known as the "temporal credit-assignment (TCA) problem". By matching the output spikes of neurons to the number of features, the aggregated-label (AL) learning is a promising approach to target this problem. However, existing mainstream AL learning algorithms either only focus on a few information of features or consider the imprecise temporal loss. In this paper, we propose a novel adaptive interval membrane potential driven aggregated-label learning algorithm, named IMPD-AL. This algorithm not only captures the adaptive interval temporal information but also integrates the loss of all spike firing times in the precise interval most relevant to features. Experimental results on speech recognition (TIDIGITS), dynamic digit recognition (N-MNIST), dynamic image recognition (CIFAR10-DVS), and dynamic gesture recognition (DVS128 Gesture) show that the IMPD-AL algorithm outperforms the state-of-the-art (SOTA) AL algorithm in SNNs on both classification task and TCA task.

关键词： Neurons clustering algorithms Membrane potentials Task analysis Training Computational modeling Heuristic algorithms Temporal credit-assignment problem aggregate-label learning spiking neural network membrane potential driven synaptic plasticity

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Joint Correlations Sparse Bayesian Learning STAP With Prior Knowledge of Clutter Ridge

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 6820-6832页

作者： Cui, Junhao Chen, Zhangxin Liang, Jing Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

Space-time adaptive processing (STAP) based on sparse Bayesian learning (SBL) can significantly improve clutter suppression performance utilizing clutter sparsity. However, the existing SBL-STAP algorithms lack full use of correlations, which leads to unsatisfactory performance and slow convergence speed. In this article, we propose a joint correlations SBL-STAP (JCSBL-STAP) algorithm to improve clutter suppression performance. It comes from a rational idea that the clutter ridge in the space-time domain is not only the origin of clutter sparsity, but also the origin of correlations. Normally, the amplitude of scatterers along the clutter ridge are correlated between multiple samples and have clustered correlation properties in each sample. The JCSBL-STAP algorithm utilizes a joint correlations sparse prior to exploiting both correlations and provides a multisample correlation decoupling framework to update hyperparameters. The algorithm is executed on a proposed hybrid prior dictionary. Compared with the conventional uniform dictionary, the hybrid prior dictionary can easily express the clustered correlation properties and effectively alleviates the off-grid problem. Experimental results confirm the performance of the proposed method on both simulated data and measured Mountain-Top data.

关键词： Clutter Correlation Vectors Training clustering algorithms Dictionaries Doppler effect Bayes methods Sparse matrices Remote sensing Dictionary design joint correlations prior knowledge of clutter ridge space-time adaptive processing (STAP) sparse Bayesian learning (SBL)

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MCC: Multi-Cluster Contrastive Semi-Supervised Segmentation Framework for Echocardiogram Videos

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IEEE ACCESS 2025年 13卷 30543-30554页

作者： Chen, Yu-Jen Lin, Shr-Shiun Shi, Yiyu Ho, Tsung-Yi Xu, Xiaowei Natl Tsing Hua Univ Dept Comp Sci Hsinchu 300044 Taiwan Chung Shan Med Univ Hosp Dept Emergency Med Taichung 40201 Taiwan Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN 46556 USA Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Southern Med Univ Guangdong Prov Peoples Hosp Guangdong Acad Med Sci Guangzhou 510080 Peoples R China

Variability in sonographer expertise often leads to low-quality ultrasound imaging, presenting significant challenges for accurate echocardiogram video segmentation. Current methods require extensive annotations, which are impractical given the large number of frames and artifacts in videos. To address this, we propose a Multi-Cluster Contrastive (MCC) learning framework, a semi-supervised approach that minimizes annotation requirements while maintaining high segmentation performance. Leveraging contrastive loss to enhance foreground feature extraction, our method incorporates multi-cluster contrastive loss to utilize multiple annotated ground-truths per batch and an anchor frame selection algorithm to improve segmentation performance. Experimental results on two public echocardiography datasets (MCE and EchoNet-Dynamic) demonstrate the effectiveness of our method, achieving state-of-the-art performance. The MCC framework enhances segmentation practicality by reducing annotation requirements, particularly for developing new datasets, and facilitates efficient segmentation of low-quality echocardiogram videos. Our implementation is available at https://***/windstormer/MCC.

关键词： Videos Annotations Image segmentation Noise Myocardium Adaptation models Training Echocardiography clustering algorithms Ultrasonic imaging Semisupervised learning object segmentation echocardiography self-supervised learning

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Reinforcing TSK Model Interpolation With Rule Weight Adjustment via Location View Analysis

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2025年第2期33卷 580-592页

作者： Jiang, Changhong Shang, Changjing Shen, Qiang Aberystwyth Univ Dept Comp Sci Aberystwyth SY23 3FL Wales

Fuzzy rule interpolation (FRI) has been successfully applied to address real-world problems where domain knowledge is incomplete, particularly in situations where observations do not directly match existing rules. However, most of the existing research focuses on fuzzy systems based on Mamdani models. Recently, a pioneering approach has been proposed for rule interpolation within Takagi-Sugeno-Kang (TSK) fuzzy models, employing two distinct techniques that cluster neighboring rules to facilitate interpolated outcomes. Although promising, these techniques have been developed through empirical methods, lacking formal representation and theoretical justification. This article formalizes such emerging empirical studies with the aim of strengthening the theoretical foundation of FRI. It introduces the location view of rules and rule bases regarding TSK models, analyzing the influence of individual rules within FRI techniques for such models. Through geometrizing sparse rule bases, comparative investigations of the empirical and theoretical results are carried out. Moreover, geometric projection of the rules is combined with a rule weight adjustment mechanism to reinforce the capability of the formalized FRI techniques. By embedding directional parameters into the rules, the resulting method increases the weighting of critical rules, strengthening the FRI algorithms' performance when many rules may be selected for interpolation. Experimental results confirm the effectiveness of the theoretical model and the consistency with the existing empirical findings. This highlights the potential and significance of formal studies in guiding algorithmic improvements on FRI with TSK models.

关键词： Interpolation Analytical models Fuzzy systems clustering algorithms Euclidean distance Data mining Complexity theory Cognition Takagi-Sugeno model Medical services Formalization fuzzy rule interpolation location view rule projection TSK model

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