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Bidirectional Authentication Protocol for RFID Systems Based on Mms

International Journal of Network Security 2025年第1期27卷 72-80页

作者： He, Xiao-ping Liu, Dao-wei Wu, Yang School of Data and Computer Science Guangdong Peizheng College Guangzhou510800 China Engineering College Guangzhou College of Technology and Business Guangzhou510006 China College of Information and Communication National University of Defense Technology Wuhan430010 China

data exchange between electronic tags and the reader-writer in RFID systems is based on the wireless channel. Due to the inherent openness of the wireless channel, the transmitted data information is easy to be eavesdropped by other illegal users. In order to ensure data security, an ultra-lightweight mutual authentication protocol is proposed to solve this problem. In this paper, we independently design a new encryption algorithm, namely the product module synthesis operation. This algorithm is implemented based on bit operations, which can greatly reduce the total computation burden. Using the inherent Hamming weight and anti-Hamming weight information of encrypted parameters, different synthesis operations are carried out based on their product size relationship. Through the protocol analysis, it is shown that the proposed protocol has high security performance and meets the current computational requirements of low-cost RFID systems. © (2025), (Femto Technique Co., Ltd.). All rights reserved.

关键词： Radio frequency identification (RFID)

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A Novel Community Detection Algorithm Based on Node Propagation and Attraction

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IEEE Access 2025年 13卷 69161-69174页

作者： Fu, Lidong Xie, Xinxing Dou, Zengfa Bai, Yun Zhao, Xi Xi'an University of Science and Technology College of Computer Science and Technology Xi'an710600 China Qinghai Institute of Technology School of Computer and Information Science Xining810016 China Xi'an University School of Information Engineering Xi'an710065 China Xi'an Big Data Service Center Xi'an710007 China

High-quality community detection in complex networks typically relies on the similarity between nodes to select communities. However, when a node exhibits the same similarity with different communities, it becomes ambiguous to determining the optimal community assignment for that node, potentially leading to incorrect predictions and affecting the accuracy of community assignments for other nodes. To address this problem, this paper proposes a novel community detection algorithm based on node propagation and attraction. The algorithm first introduces a node propagation strategy, which identifies high-propagation nodes based on their connectivity and core attributes. These high-propagation nodes form the initial backbone and framework of communities, minimizing the interference of low-propagation nodes on community structure. Subsequently, an attraction definition is introduced, which starts from high-propagation nodes to select partitioning methods based on their relationships with neighboring nodes, using the attraction among these nodes facilitate community organization and propagation, aiming to enhance community quality. Finally, in the community optimization phase, the algorithm effectively improves the accuracy of partitions that result from similar attractions by considering the association degree between nodes and communities. Experimental results demonstrate that, on both synthetic and real networks, the proposed algorithm outperforms various existing community detection algorithms. © 2013 IEEE.

关键词： Optimization algorithms

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A Novel Reliable and Trust Objective Function for RPL-Based IoT Routing Protocol

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computers, Materials & Continua 2025年第2期82卷 3467-3497页

作者： Mariam A.Alotaibi Sami S.Alwakeel Aasem N.Alyahya Department of Computer Engineering College of Computer ScienceKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science and Information Technology Shaqra UniversityShaqra11961Saudi Arabia

The Internet of Things (IoT) integrates diverse devices into the Internet infrastructure, including sensors, meters, and wearable devices. Designing efficient IoT networks with these heterogeneous devices requires the selection of appropriate routing protocols, which is crucial for maintaining high Quality of Service (QoS). The Internet Engineering Task Force’s Routing Over Low Power and Lossy Networks (IETF ROLL) working group developed the IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) to meet these needs. While the initial RPL standard focused on single-metric route selection, ongoing research explores enhancing RPL by incorporating multiple routing metrics and developing new Objective Functions (OFs). This paper introduces a novel Objective Function (OF), the Reliable and Secure Objective Function (RSOF), designed to enhance the reliability and trustworthiness of parent selection at both the node and link levels within IoT and RPL routing protocols. The RSOF employs an adaptive parent node selection mechanism that incorporates multiple metrics, including Residual Energy (RE), Expected Transmission Count (ETX), Extended RPL Node Trustworthiness (ERNT), and a novel metric that measures node failure rate (NFR). In this mechanism, nodes with a high NFR are excluded from the parent selection process to improve network reliability and stability. The proposed RSOF was evaluated using random and grid topologies in the Cooja Simulator, with tests conducted across small, medium, and large-scale networks to examine the impact of varying node densities. The simulation results indicate a significant improvement in network performance, particularly in terms of average latency, packet acknowledgment ratio (PAR), packet delivery ratio (PDR), and Control Message Overhead (CMO), compared to the standard Minimum Rank with Hysteresis Objective Function (MRHOF).

关键词： IoT LLNs RPL objective function OF MRHOF OF0 routing metrics reliability trustworthiness

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Algorithms for online fault tolerance server consolidation

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Digital Communications and Networks 2025年第2期11卷 514-523页

作者： Boyu Li Bin Wu Meng Shen Hao Peng Weisheng Li Hong Zhang Jie Gan Zhihong Tian Guangquan Xu College of Intelligence and Computing Tianjin University School of Cyberspace Science and Technology Beijing Institute of Technology School of Computer Science and Technology Zhejiang Normal University School of Computer Science and Technology Chongqing University of Posts and Telecommunications School of Astronautics Beihang University School of Computer Science University of Sydney Cyberspace Institute of Advanced Technology Guangzhou University

We study a novel replication mechanism to ensure service continuity against multiple simultaneous server failures. In this mechanism, each item represents a computing task and is replicated into ξ + 1 servers for some integer ξ ≥ 1, with workloads specified by the amount of required resources. If one or more servers fail, the affected workloads can be redirected to other servers that host replicas associated with the same item, such that the service is not interrupted by the failure of up to ξ servers. This requires that any feasible assignment algorithm must reserve some capacity in each server to accommodate the workload redirected from potential failed servers without overloading, and determining the optimal method for reserving capacity becomes a key issue. Unlike existing algorithms that assume that no two servers share replicas of more than one item, we first formulate capacity reservation for a general arbitrary scenario. Due to the combinatorial nature of this problem, finding the optimal solution is difficult. To this end, we propose a Generalized and Simple Calculating Reserved Capacity(GSCRC) algorithm, with a time complexity only related to the number of items packed in the server. In conjunction with GSCRC, we propose a robust replica packing algorithm with capacity optimization(RobustPack), which aims to minimize the number of servers hosting replicas and tolerate multiple server failures. Through theoretical analysis and experimental evaluations, we show that the RobustPack algorithm can achieve better performance.

关键词： Cloud computing Server consolidation Replica Fault tolerance

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Enhancing Predictive Capabilities for Cyber Physical Systems Through Supervised Learning

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第16期49卷 77-86页

作者： Dhanalakshmi, B. Tamije Selvy, P. Department of Computer Science and Engineering Dr.N.G. P Institute of technology India Department of Computer Science and Engineering Hindusthan College of Engineering and Technology India

The rapid advancement and proliferation of Cyber-Physical Systems (CPS) have led to an exponential increase in the volume of data generated continuously. Efficient classification of this streaming data is crucial for predicting system behaviors and enabling proactive decision-making. This research aims to extract actionable knowledge from the continuous data streams of CPS and predict their behavior using advanced supervised learning algorithms. The predictions facilitate timely interventions and necessary actions within the interconnected physical network. The background of this work lies in the intersection of CPS, machine learning, and data stream mining. Traditional batch processing methods are inadequate for real-time analysis of CPS data due to their inherent latency and computational inefficiency. This research employs state-of-the-art techniques for real-time data processing, including incremental learning, sliding window models, and ensemble methods tailored for streaming data. Our approach differs from existing works by focusing on a comprehensive framework that integrates real-time data ingestion, preprocessing, feature extraction, and model updating in a seamless pipeline. Unlike previous studies that often rely on static datasets and offline analysis, our method ensures continuous learning and adaptation to evolving data patterns. Comparative analysis with existing techniques demonstrates superior performance in terms of accuracy, latency, and scalability. Specifically, our models achieved an average classification accuracy of 92%, with a precision of 90%, recall of 89%, and an F1 score of 89.5%. These metrics indicate significant improvements over traditional batch processing methods, which typically lag in responsiveness and adaptability. This research provides a robust and efficient solution for the real-time classification of streaming data from CPS, enhancing the system's ability to predict behaviors and take necessary actions promptly. © 2025 Slov

关键词： Self-supervised learning

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A Single Source Generalization Model via Spatial Amplitude Perturbation and Sensitivity Guidance for Colored Medical Image Segmentation 27th

A Single Source Generalization Model via Spatial Amplitude ...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Yang, Zeyuan Yu, Chunyan College of Computer and Data Science/College of Software Fuzhou University Fuzhou350108 China

ISBN: (纸本)9783031782008

For medical images, domain shift is a very common phenomenon. To address this issue, researchers have proposed unsupervised domain adaptation and multi-source domain generalization. However, these methods are sometimes impractical for clinical applications since they need multi-domain data. To this end, single-source domain generalization has been further proposed. However, most single-source domain generalization methods are designed for grayscale medical images, making them unsuitable for color images such as fundus images. In this paper, we first propose a novel and effective Fourier transform-based data augmentation method for single-source domain color medical images, named spatial amplitude perturbation module (SAPM). The SAPM uses different Gaussian distributions to perturb different regions of the amplitude map obtained by FFT decomposition, thereby avoiding the need for information from other domains and ensuring the diversity of the augmented images. Then, we use feature sensitivity to guide the network to learn domain-invariant features, which can suppress feature channels sensitive to domain shift and emphasize feature channels insensitive to domain shift. We evaluate our method on a multi-domain fundus segmentation benchmark, and the results demonstrate the effectiveness of our proposed method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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Optimizing Coverage in Wireless Sensor Networks Using the Tumbleweed Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第1期10卷 250-261页

作者： Zhang, Mengfei Chu, Shu-Chuan Liu, Nengxian Huang, Kuan-Chun Pan, Jeng-Shyang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Department of Industrial and Systems Engineering Chung Yuan Christian University Taoyuan320314 Taiwan School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing210044 China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan

Wireless sensor networks (WSNs) have found extensive applications across various fields, significantly enhancing the convenience in our daily lives. Hence, an in-creasing number of researchers are directing their attention toward the performance of sensor nodes, with a particular emphasis on addressing the coverage challenges in WSNs. Node coverage plays a crucial role in determining the detection performance of WSNs, making it imperative to attain maximum coverage with a given number of sensor nodes. This paper applies the Tumbleweed Algorithm (TA) to the coverage problem in WSNs and validates its effectiveness through simulation experiments. The results demonstrate its unique advantages in global search and avoiding local optima, providing a new solution for the WSN coverage problem and offering valuable references for future research and practical applications. The TA algorithm simulates the lifecycle of tumbleweed from seedling to mature plant and includes the concept of growth cycles. This paper compares the TA algorithm with PSO, GWO, DE, BOA, SCA, and AOA, setting different numbers of wireless sensor nodes and varying sensing radii for the experiments. The experimental results show that the TA algorithm significantly improves coverage rates under various configurations and consistently outperforms other commonly used algorithms. © 2025, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Sensor nodes

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A Secret Sharing-Inspired Robust Distributed Backdoor Attack to Federated Learning

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ACM Transactions on Privacy and Security 2025年第2期28卷 1-19页

作者： Yang, Yuxin Li, Qiang Ji, Yuede Wang, Binghui College of Computer Science and Technology Jilin University Changchun China Department of Computer Science Illinois Institute of Technology Chicago United States Department of Computer Science and Engineering The University of Texas at Arlington Arlington United States Department of Computer Science Illinois Institute of Technology Chicago United States

Federated Learning (FL) is vulnerable to backdoor attacks - especially distributed backdoor attacks (DBA) that are more persistent and stealthy than centralized backdoor attacks. However, we observe that the attack effectiveness of DBA can be largely reduced when encountering rebels, i.e., the agents promising to perform the attack but do not do so. To robustify DBAs, we present SSRDBA, a secret sharing-inspired robust DBA to FL. To be specific, given a same global trigger as DBA, SSRDBA carefully divides it into different shares based on secret sharing and exploits these shares to poison local data on malicious devices, respectively. SSRDBA enjoys several merits, e.g., only partial malicious agents guarantee the reconstruction of the global trigger. Extensive experimental results show that SSRDBA is more robust to rebels than DBA and can evade the state-of-the-art FL defenses mainly for centralized backdoor attacks. To mitigate SSRDBA, we further design a novel defense mechanism, termed NFDR, which shows great potential against SSRDBAon certain independent identically distributed datasets. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Federated learning

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Blockchain and signcryption enabled asynchronous federated learning framework in fog computing

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Digital Communications and Networks 2025年第2期11卷 442-454页

作者： Zhou Zhou Youliang Tian Jinbo Xiong Changgen Peng Jing Li Nan Yang State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fujian Normal University Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology

Federated learning combines with fog computing to transform data sharing into model sharing, which solves the issues of data isolation and privacy disclosure in fog computing. However, existing studies focus on centralized single-layer aggregation federated learning architecture, which lack the consideration of cross-domain and asynchronous robustness of federated learning, and rarely integrate verification mechanisms from the perspective of incentives. To address the above challenges, we propose a Blockchain and Signcryption enabled Asynchronous Federated Learning(BSAFL) framework based on dual aggregation for cross-domain scenarios. In particular, we first design two types of signcryption schemes to secure the interaction and access control of collaborative learning between domains. Second, we construct a differential privacy approach that adaptively adjusts privacy budgets to ensure data privacy and local models' availability of intra-domain user. Furthermore, we propose an asynchronous aggregation solution that incorporates consensus verification and elastic participation using blockchain. Finally, security analysis demonstrates the security and privacy effectiveness of BSAFL, and the evaluation on real datasets further validates the high model accuracy and performance of BSAFL.

关键词： Blockchain Signcryption Federated learning Asynchronous Fog computing

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SrTLS: Secure TLS Handshake on Corrupted Machines

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 3137-3154页

作者： Liu, Jiahao Wang, Yi Tang, Xincheng Chen, Rongmao Huang, Xinyi Su, Jinshu National University of Defense Technology College of Computer Science and Technology Hunan Changsha410073 China Jinan University College of Cyber Security Guangzhou China Academy of Military Science Beijing China

TLS 1.3 is widely used to realize secure communication over the Internet. Existing security analyses of TLS 1.3 primarily focus on its handshake protocol which is indeed an authenticated key exchange (AKE) protocol, and implicitly neglect the so-called subversion attacks (e.g., breaking TLS via Dual EC) in the real world. Reverse firewall (RF) is a prevalent approach to defend against subversion attack. To the best of our knowledge, the only two subversion-resilient AKE protocols with RFs are proposed by Dodis et al. (CRYPTO'16) and Bossuat et al. (ESORICS'20). The security of both protocols is proved under game-based model which is insufficient for the concurrent execution of multiple TLS instances in practice. In this paper, we propose srTLS, a variant of the TLS 1.3 full one round-trip time (1-RTT) handshake protocol with RFs under the universally composable (UC) model. In particular, we first present the ideal functionality of unilateral AKE FuaKE. Then, we use RFs with outer transparency to circumvent the difficulty in sanitizing the messages of handshake protocol, and prove that srTLS UC-realizes FuaKE in the presence of subversion attacks. Finally, we integrate srTLS and existing subversion-resilient AKE protocols into TLS 1.3. The evaluation result demonstrates that srTLS achieves at least a 44.86% efficiency improvement over other subversion-resilient AKE protocols. © 2004-2012 IEEE.

关键词： Authentication Protocol

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