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Towards Resilience 5G-V2N: Efficient and Privacy-Preserving Authentication Protocol for Multi-Service Access and Handover

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IEEE Transactions on Mobile Computing 2025年第06期24卷 5446-5463页

作者： Bi, Ye Jia, Chunfu Nankai University College of Cyber Science Tianjin Key Laboratory of Network and Data Security Technology DISSEC Tianjin300350 China

The booming 5 G cellular networks sparked tremendous interest in supporting more sophisticated critical use cases through vehicle-to-network (V2N) communications. However, the inherent technical vulnerabilities and densification of 5 G raise new security and efficiency challenges. The existing secondary authentication fails to support multi-service access. The random access process lacks authentication of the gNB, possibly leading to fake base station attacks (FBS). Moreover, related research extends key forward/backward secrecy (KF/BS) to require that it also applies to gNBs, thus invalidating most existing schemes. This paper introduces a comprehensive security framework for 5G-V2N that seamlessly integrates with existing standardized architecture to provide privacy-preserving mutual authentication and key agreement for the full service cycle. Specifically, we propose new secondary authentication involving gNBs and support single request access to multi-services. Second, incorporating the service migration idea, we design the g2g (gNB-to-gNB) channel establishment phase to promote secure context share. Finally, the proposed efficient handover phase achieves the security properties of enhanced KF/BS, known randomness secrecy and privacy-preserving, and avoids FBS. We verify the proposed protocol using three different formal techniques: provably secure, BAN-logic, and AVISPA tool. Extensive experimental results and comparison show that our scheme excels in computational and communication efficiencies, and detecting malicious events. © 2025 IEEE.

关键词： Authentication Protocol

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Block-gram:Mining knowledgeable features for efficiently smart contract vulnerability detection

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Digital Communications and networks 2025年第1期11卷 1-12页

作者： Xueshuo Xie Haolong Wang Zhaolong Jian Yaozheng Fang Zichun Wang Tao Li Tianjin Key Laboratory of Network and Data Security Technology TianjinChina College of Computer Science Nankai UniversityTianjinChina Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province China State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesChina

Smart contracts are widely used on the blockchain to implement complex transactions,such as decentralized applications on *** vulnerability detection of large-scale smart contracts is critical,as attacks on smart contracts often cause huge economic *** it is difficult to repair and update smart contracts,it is necessary to find the vulnerabilities before they are ***,code analysis,which requires traversal paths,and learning methods,which require many features to be trained,are too time-consuming to detect large-scale on-chain ***-based methods will obtain detection models from a feature space compared to code analysis methods such as symbol *** the existing features lack the interpretability of the detection results and training model,even worse,the large-scale feature space also affects the efficiency of *** paper focuses on improving the detection efficiency by reducing the dimension of the features,combined with expert *** this paper,a feature extraction model Block-gram is proposed to form low-dimensional knowledge-based features from ***,the metadata is separated and the runtime code is converted into a sequence of opcodes,which are divided into segments based on some instructions(jumps,etc.).Then,scalable Block-gram features,including 4-dimensional block features and 8-dimensional attribute features,are mined for the learning-based model ***,feature contributions are calculated from SHAP values to measure the relationship between our features and the results of the detection *** addition,six types of vulnerability labels are made on a dataset containing 33,885 contracts,and these knowledge-based features are evaluated using seven state-of-the-art learning algorithms,which show that the average detection latency speeds up 25×to 650×,compared with the features extracted by N-gram,and also can enhance the interpretability of the detection model.

关键词： Smart contract Bytecode&opcode Knowledgeable features Vulnerability detection Feature contribution

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More trust in networks,more secure keys

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Light(science & Applications) 2025年第1期14卷 18-19页

作者： Guan-Jie Fan-Yuan Shuang Wang CAS Key Laboratory of Quantum Information University of Science and Technology of China230026HefeiChina Anhui Province Key Laboratory of Quantum Network University of Science and Technology of China230026HefeiChina CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China230026HefeiChina Hefei National Laboratory University of Science and Technology of China230088HefeiChina

A novel continuous-variable quantum passive optical network is proposed in which a user can increase their key rate by trusting other *** is because the keys,which would be discarded to remove correlations with untrus... 详细信息

关键词： network passive networks

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Large Language Model-Based Event Relation Extraction with Rationales 31

Large Language Model-Based Event Relation Extraction with Ra...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Hu, Zhilei Li, Zixuan Jin, Xiaolong Bai, Long Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of SciencesSchool of Computer Science and Technology University of Chinese Academy of Sciences

ISBN: (纸本)9798891761964

Event Relation Extraction (ERE) aims to extract various types of relations between different events within texts. Although Large Language Models (LLMs) have demonstrated impressive capabilities in many natural language processing tasks, existing ERE methods based on LLMs still face three key challenges: (1) Time Inefficiency: The existing pairwise method of combining events and determining their relations is time-consuming for LLMs. (2) Low Coverage: When dealing with numerous events in a document, the limited generation length of fine-tuned LLMs restricts the coverage of their extraction results. (3) Lack of Rationale: Essential rationales concerning the results that could enhance the reasoning ability of the model are overlooked. To address these challenges, we propose LLMERE, an LLM-based approach with rationales for the ERE task. LLMERE transforms ERE into a question-and-answer task that may have multiple answers. By extracting all events related to a specified event at once, LLMERE reduces time complexity from O(n2) to O(n), compared to the pairwise method. Subsequently, LLMERE enhances the coverage of extraction results by employing a partitioning strategy that highlights only a portion of the events in the document at a time. In addition to the extracted results, LLMERE is also required to generate corresponding rationales/reasons behind them, in terms of event coreference information or transitive chains of event relations. Experimental results on three widely used datasets show that LLMERE achieves significant improvements over baseline methods. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Underwater target recognition based on adaptive multi-feature fusion network

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Multimedia Tools and Applications 2025年第10期84卷 7297-7317页

作者： Pan, Xiaoying Sun, Jia Feng, TianHao Lei, MingZhu Wang, Hao Zhang, WuXia Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an710121 China Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’an710121 China School of Computer Science and Technology Xi’an University of Post & Telecommunications Xi’an710121 China

Due to the complexity of the underwater environment, underwater acoustic target recognition is more challenging than ordinary target recognition, and has become a hot topic in the field of underwater acoustics research. In recent years, deep learning has been widely used in underwater recognition due to its powerful feature learning capabilities. However, most deep learning-based underwater target recognition methods use only the time-frequency domain features of signals, ignoring the frequency domain image features of signals. Additionally, they only use simple summation or splicing strategies to fuse and discriminate the extracted features, which leads to limited improvement in the accuracy of underwater target recognition. To address these issues, this paper proposes an underwater target recognition method based on an adaptive multi-feature fusion network. The method consists of a data pre-processing module, a multi-dimensional feature extraction module, and an adaptive multi-feature fusion module. In the multi-dimensional feature extraction module, the long short time memory network (LSTM) and the one-dimensional convolutional neural network (1DCNN) are used to extract the time-frequency features of the underwater signal. Furthermore, the two-dimensional convolutional neural network (2DCNN) is used to extract the image features in the frequency domain. The adaptive multi-feature fusion module uses an attention mechanism for adaptive weighted fusion to make full use of the learned features. The effectiveness of the proposed method is validated on the ShipEar dataset, and the recognition accuracy reaches 94.92%, which is higher than other existing methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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A Novel Approach to Cloud Security: Publicly Verifiable Remote Signcryption Framework

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IEEE Internet of Things Journal 2025年第10期12卷 14828-14844页

作者： Ali, Mohammad Liu, Ximeng Amirkabir University of Technology Department of Mathematics and Computer Science Tehran Iran Fuzhou University Key Laboratory of Information Security of Network Systems College of Computer and Data Science Fuzhou350108 China

In communication systems, ensuring both data integrity and confidentiality is crucial. Traditional methods, such as encryption and signature schemes, handle these aspects separately, leading to inefficiencies. Signcryption techniques, which address both requirements simultaneously, present a more efficient alternative. Nonetheless, they are unsuitable for cloud storage systems due to their reliance on original files for data auditing. Remote data integrity checking (RDIC) methods resolve this issue but compromise data confidentiality. To bridge the gap between signcryption and RDIC approaches, remote signcryption (RSC) has been proposed. However, existing RSC methods lack support for public auditing, rendering data auditing non-outsourcable. This paper introduces publicly verifiable remote signcryption (PVRSC) to address this shortcoming and presents the first concrete PVRSC scheme with public verifiability. We define the security model for PVRSC and prove its robustness based on the hardness of the Bilinear Diffie-Hellman (BDH) problem. Our implementation on real data shows that PVRSC significantly outperforms existing methods, achieving speeds up to 100 times faster and reducing communication costs by half compared to RDIC methods. For outsourced data up to 5MB, PVRSC maintains a communication cost below 8KB during data recovery, a substantial improvement over classical signcryption approaches, which exceed 5MB. © 2014 IEEE.

关键词： Cloud security

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Robust Information Retrieval 25

Robust Information Retrieval

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18th ACM International Conference on Web Search and data Mining, WSDM 2025

作者： Liu, Yu-An Zhang, Ruqing Guo, Jiafeng de Rijke, Maarten CAS Key Lab of Network Data Science and Technology ICT CAS Beijing China University of Chinese Academy of Sciences Beijing China University of Amsterdam Amsterdam Netherlands

ISBN: (纸本)9798400713293

Beyond effectiveness, the robustness of an information retrieval (IR) system is increasingly attracting attention. When deployed, a critical technology such as IR should not only deliver strong performance on average but also have the ability to handle a variety of exceptional situations. In recent years, research into the robustness of IR has seen significant growth, with numerous researchers offering extensive analyses and proposing myriad strategies to address robustness challenges. In this tutorial, we first provide background information covering the basics and a taxonomy of robustness in IR. Then, we examine adversarial robustness and out-of-distribution (OOD) robustness within IR-specific contexts, extensively reviewing recent progress in methods to enhance robustness. The tutorial concludes with a discussion on the robustness of IR in the context of large language models (LLMs), highlighting ongoing challenges and promising directions for future research. This tutorial aims to generate broader attention to robustness issues in IR, facilitate an understanding of the relevant literature, and lower the barrier to entry for interested researchers and practitioners. © 2025 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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ADLT: a novel method for effective detection of adversarial attacks in image retrieval

ADLT: a novel method for effective detection of adversarial ...

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2024 International Conference on Computer Vision and Image Processing, CVIP 2024

作者： Xu, Shiyue Zhang, Yu College of Cyber Science Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin300350 China

ISBN: (纸本)9781510688384

The versatile application of deep learning has made it prone to vulnerabilities, particularly adversarial attacks, which have been increasingly prevalent as its scope expands. Attack methods based on adversarial queries pose significant challenges to image retrieval models, disrupting their functionality. While numerous defense methods have been proposed for adversarial query attacks in image classification, approaches to detecting or defending against such attacks in image retrieval remain limited due to inherent challenges in adapting these methods. Based on these observations, we introduce the Augmentation Detection based on Top-K-List (ADTL) method, the first effective approach for detecting adversarial attacks in image retrieval. ADTL applies image augmentation to a query and determines its adversarial nature by calculating the divergence in the Top-K list before and after augmentation. This paper presents a method that employs suitable image augmentation techniques and K-Reciprocal clustering for pre-classifying images in the gallery to ease divergence calculations, which has been tested on the Oxford5k and Paris6k datasets and has shown remarkable effectiveness with a high detection accuracy of 87.3%. © 2025 SPIE.

关键词： Adversarial perturbation Attack detection Image augmentation Image retrieval

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Crowdsense Roadside Parking Spaces With Dynamic Gap Reduction Algorithm

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IEEE Internet of Things Journal 2025年第9期12卷 11858-11873页

作者： Zheng, Wenjun Shi, Zhan Ou, Qianyu Liao, Ruizhi The Chinese University of Hong Kong School of Data Science Shenzhen Shenzhen518116 China Guangdong Provincial Key Laboratory of Mathematical Foundations for Artificial Intelligence Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology School of Humanities and Social Science Shenzhen518116 China

In the context of smart city development, mobile sensing emerges as a cost-effective alternative to fixed sensing for on-street parking detection. However, its practicality is often challenged by the inherent accuracy limitations arising from detection intervals. This article introduces a novel dynamic gap reduction algorithm (DGRA), which is a crowdsensing-based approach aimed at addressing this question through parking detection data collected by sensors on moving vehicles. The algorithm’s efficacy is validated through real drive tests and simulations. We also present a driver-side and traffic-based evaluation model (DSTBM), which incorporates drivers’ parking decisions and traffic conditions to evaluate DGRA’s performance. Results highlight DGRA’s significant potential in reducing the mobile sensing accuracy gap, marking a step forward in efficient urban parking management. © 2014 IEEE.

关键词： Parking

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Inductive Link Prediction in N-ary Knowledge Graphs 31

Inductive Link Prediction in N-ary Knowledge Graphs

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wei, Jiyao Guan, Saiping Jin, Xiaolong Guo, Jiafeng Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9798891761964

N-ary Knowledge Graphs (NKGs), where a fact can involve more than two entities, have gained increasing attention. Link Prediction in NKGs (LPN) aims to predict missing elements in facts to facilitate the completion of NKGs. Current LPN methods implicitly operate under a closed-world assumption, meaning that the sets of entities and roles are fixed. These methods focus on predicting missing elements within facts composed of entities and roles seen during training. However, in reality, new facts involving unseen entities and roles frequently emerge, requiring completing these facts. Thus, this paper proposes a new task, Inductive Link Prediction in NKGs (ILPN), which aims to predict missing elements in facts involving unseen entities and roles in emerging NKGs. To address this task, we propose a Meta-learning-based Nary knowledge Inductive Reasoner (MetaNIR), which employs a graph neural network with meta-learning mechanisms to embed unseen entities and roles adaptively. The obtained embeddings are used to predict missing elements in facts involving unseen elements. Since no existing dataset supports this task, three datasets are constructed to evaluate the effectiveness of MetaNIR. Extensive experimental results demonstrate that MetaNIR consistently outperforms representative models across all datasets. © 2025 Association for Computational Linguistics.

关键词： Knowledge graph

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