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Enabling Intelligent Connectivity: A Survey of Secure ISAC in 6G networks

IEEE Communications Surveys and Tutorials 2025年第2期27卷 748-781页

作者： Zhu, Xiaoqiang Liu, Jiqiang Lu, Lingyun Zhang, Tao Qiu, Tie Wang, Chunpeng Liu, Yuan Beijing Jiaotong University School of Software Engineering Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing100044 China Tianjin University College of Intelligence and Computing Tianjin300350 China Qilu University of Technology School of Cyber Security and the Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250353 China

The rapid growth of intelligent sensing capabilities and super computation power in 6G mobile communication systems has facilitated their application in diverse domains such as smart health, smart factories, and the industrial Internet of Things. Integrated Sensing and Communication (ISAC), as a core technology, has merged with artificial intelligence (AI) to enable intelligent connectivity, leading to a paradigm shift in traditional communication modes. This paper presents a visionary design for an ISAC-oriented unified IoT architecture that integrates software-defined communication and super-intelligent agents. By leveraging dynamic adaptability, self-learning, and optimization, the ISAC system can intelligently and flexibly respond to evolving requirements and environments. The architecture is redefined into three layers: the hardware layer, the omniscient layer, and the application layer. Furthermore, a retrospective survey of ISAC technology development over the past decade is conducted, highlighting new design principles for AI-empowered networks and multi-modals that support "intelligent connectivity" across various application scenarios and reinforce the security of ISAC. This paper categorizes the related works according to the different layer structures of the proposed architecture, and some important physical and machine learning models are introduced. Additionally, we summarize the current technological bottlenecks associated with ISAC and propose future research directions and potential solutions that lay the foundation for the future development of secure and intelligent communication networks. © 1998-2012 IEEE.

关键词： Computer architecture

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Fusion of Time-Frequency Features in Contrastive Learning for Shipboard Wind Speed Correction

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Journal of Ocean University of China 2025年第2期24卷 377-386页

作者： SONG Jian HUANG Meng LI Xiang ZHANG Zhenqiang WANG Chunxiao ZHAO Zhigang Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250000China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250000China

Accurate wind speed measurements on maritime vessels are crucial for weather forecasting,sea state prediction,and safe ***,vessel motion and challenging environmental conditions often affect measurement *** address this issue,this study proposes an innovative framework for correcting and predicting shipborne wind *** integrating a main network with a momentum updating network,the proposed framework effectively extracts features from the time and frequency domains,thereby allowing for precise adjustments and predictions of shipborne wind speed *** using real sensor data collected at the Qingdao Oceanographic Institute demonstrates that the proposed method outperforms existing approaches in single-and multi-step predictions compared to existing methods,achieving higher accuracy in wind speed *** proposed innovative approach offers a promising direction for future validation in more realistic maritime onboard scenarios.

关键词： time series prediction wind speed correction comparative learning shipborne sensor

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A Chain-of-Task Framework for Instruction Tuning of LLMs Based on Chinese Grammatical Error Correction 31

A Chain-of-Task Framework for Instruction Tuning of LLMs Bas...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Liu, Xinpeng Xu, Bing Yang, Muyun Cao, Hailong Zhu, Conghui Zhao, Tiejun Lu, Wenpeng Faculty of Computing Harbin Institute of Technology Harbin China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences Jinan China

ISBN: (纸本)9798891761964

Over-correction is a critical issue for large language models (LLMs) to address Grammatical Error Correction (GEC) task, esp. for Chinese. This paper proposes a Chain-of-Task (CoTask) framework to reduce over-correction. The CoTask framework is applied as multi-task instruction tuning of LLMs by decomposing the process of grammatical error analysis to design auxiliary tasks and adjusting the types and combinations of training tasks. A supervised fine-tuning (SFT) strategy is also presented to enhance the performance of LLMs, together with an algorithm for automatic dataset annotation to avoid additional manual costs. Experimental results demonstrate that our method achieves new state-of-the-art results on both FCGEC (in-domain) and NaCGEC (out-of-domain) test sets. © 2025 Association for Computational Linguistics.

关键词： Chains

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Anomaly Detection in network Access-Using LSTM and Encoder-Enhanced Generative Adversarial networks

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第7期49卷 175-186页

作者： Hu, Jian He, Yingjun Xu, Wenqian Jiang, Yixin Liang, Zhihong Yang, Yiwei Yunnan Power Grid Information Center China Southern Power Grid Kunming650217 China Electric Power Research Institute China Southern Power Grid Guangzhou510663 China Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou510663 China

Along with the continuous development of information technology, the database has become an important module for enterprises and individuals to apply computers, and some important data are stored in the database, which also leads to the database becoming the target of malicious intruders. The abnormal access behavior detection algorithm for data can quickly identify abnormal access situations, timely intervention and processing to ensure data security. Based on this, this paper proposes an abnormal defense behavior detection algorithm based on generative adversarial network, the new algorithm has the applicability as well as two derivative models of generative adversarial network for network abnormal access detection with high efficiency. In this paper, we experiment the classification accuracy of network anomaly detection algorithm, i.e., F1, by using three models, namely, the original Generative Adversarial network (GAN), Generative Adversarial network using Long and Short-Term Memory network (GAN+LSTM), and Generative Adversarial network with the addition of an encoder (GAN+Encoder), and the result of this paper shows that the GAN+Encoder model is the most effective. And based on these three models of the generator and discriminator loss trends are compared, as well as through the iteration of 50 times of training results show that the GAN + Encoder model is relatively simple, and the training time is shorter and more efficient. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Generative adversarial networks

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Multiple Edge Data Integrity Verification With Multi-Vendors and Multi-Servers in Mobile Edge computing

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IEEE Transactions on Mobile computing 2025年第6期24卷 4668-4683页

作者： Islam, Md Rashedul Xiang, Yong Uddin, Md Palash Zhao, Yao Kua, Jonathan Gao, Longxiang Deakin University School of Information Technology GeelongVIC3220 Australia Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250316 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China

Ensuring Edge Data Integrity (EDI) is imperative in providing reliable and low-latency services in mobile edge computing. Existing EDI schemes typically address single-vendor (App Vendor, AV) single-server (Edge Server, ES), single-vendor multi-server, and multi-vendor multi-server scenarios, which consider a single data replica cached by an ES from the AVs. However, the most practical scenario of Multi-Vendors and Multi-Servers with Multiple Data (MVMS-MD) cached by an ES from different AVs remains unexplored. Current solutions struggle when applied to this scenario due to increased computation and communication costs in the verification process across all ESs using the classical challenge-response per-data multi-round strategy. To tackle this issue, we propose a Multiple EDI-Verification (MEDI-V) approach in this paper. In particular, our MEDI-V utilizes an adaptive Merkle Hash Tree (ad-MHT) to efficiently generate a tree of multiple data replicas within each AV. Next, the dynamic mechanism computes minimal verification information using ad-MHT to create a challenge for individual ESs to produce EDI proofs. The ES then leverages its ad-MHT and the ES’s proof to send the reconstructed ad-MHT root to the AV for verification. Theoretical insights into MEDI-V’s correctness, efficiency, security, and comprehensive evaluations demonstrate its superiority in addressing MEDI issues in the MVMS-MD scenario. © 2002-2012 IEEE.

关键词： Data integrity

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k-TEVS: A k-Times E-Voting Scheme on Blockchain With Supervision

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IEEE Transactions on Dependable and Secure computing 2025年第3期22卷 2326-2337页

作者： Liu, Yang He, Debiao Luo, Min Wang, Lianhai Peng, Cong Wuhan University School of Cyber Science and Engineering Wuhan430072 China Shenzhen Institute of Information Technology Institute of Information Technology Shenzhen518172 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Matrix Elements Technologies Shanghai Technology Innovation Centre of Distributed Privacy-Preserving Artificial Intelligence Shanghai200232 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250014 China

Thee-vote is regarded as a waytoexpress the opinion that the voters ask for. Actually, the e-vote could be applied wildly likequestionnaire,***,thecoexistences of efficiency and security as well as transparency and privacy could be considered as building blocks in the e-vote system. The blockchain could provide a public access board to reduce the storagecostsforthefieldconsistedofthevotegroupmanager(GM) with its vote assistants (VA). Particularly the k-times anonymous authentication (k-TAA) could also be a practical approach to pre serve voters’ privacy and reduce the computation costs during the ***,thee-voteschemewithpurek-TAAstrategy could damage either the supervision of voting or the efficiency and consistency of authentication process. What’s more, the impacts of dishonest voters couldn’t be stopped until the vote end. To tackle these problems, we apply the accumulator technology to add or revoke the voters at any time and extend the framework of k-TAA with the update process for the e-vote on blockchain for supervision (k-TEVS). In our scheme, the voter updates his membership witness and proves the fact that he is still a valid member with respective VA under the latest accumulator value. What’s more, this witness update operation is not contained in the authentication process, which means that the authentication process is still constant and efficient. Moreover, our add or delete update process with signature of knowledge needs only one pairing operation. For the security, we prove that the relaxed anonymity still holds in the k-TEVS framework. Finally, We implement k TEVS scheme, the Emura’s work [1] and the Huang’s work [2] for comparison. Then we make time cost and communication cost experiments, which present the feasibility and practicality of this scheme. © 2004-2012 IEEE.

关键词： Efficiency

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MMH-FE:AMulti-Precision and Multi-Sourced Heterogeneous Privacy-Preserving Neural network Training Based on Functional Encryption

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Computers, Materials & Continua 2025年第3期82卷 5387-5405页

作者： Hao Li Kuan Shao Xin Wang Mufeng Wang Zhenyong Zhang The State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science CenterQilu University of Technology(Shandong Academy of Sciences)Jinan250014China China Industrial Control Systems Cyber Emergency Response Team Beijing100040China

Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model ***,dishonest clouds may infer user data,resulting in user data *** schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing *** address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training ***,we design a multi-precision functional encryption computation based on Euclidean ***,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced ***,we conduct experiments on three *** results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.

关键词： Functional encryption multi-sourced heterogeneous data privacy preservation neural networks

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Source-free Domain Adaptation with Multiple Alignment for Efficient Image Retrieval

Source-free Domain Adaptation with Multiple Alignment for Ef...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Ni, Jinghui Cui, Hui Zhao, Lihai Li, Fengling Han, Xiaohui Xu, Lijuan Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science China Quan Cheng Laboratory China Shandong Open University China University of Technology Sydney Australia

ISBN: (纸本)9798350368741

Domain adaptation techniques help models generalize to target domains by addressing domain discrepancies between the source and target domain data distributions. These techniques are particularly valuable for cross-domain hashing retrieval, as they reduce training costs while maintaining high retrieval efficiency. However, existing unsupervised domain adaptative hashing methods often require access to both source and target domain data, which may raise privacy concerns regarding source domain data. To address these concerns, restricting access to source domain data is crucial, but this restriction also makes the alignment process between domains more challenging. In this paper, we propose a Source-free Domain Adaptive Hashing with Multiple Alignment (SFDAH-MA) approach for image retrieval. SFDAH-MA integrates model structure alignment, class moment alignment, and semantic relationship alignment to maximize inter-domain knowledge transfer. This comprehensive alignment strategy not only enhances retrieval performance across domains but also minimizes reliance on source domain data, thereby supporting data privacy protection. Extensive experiments show that SFDAH-MA achieves state-of-the-art performance in source-free settings and achieves comparable results to existing unsupervised domain adaptive hashing methods under conventional settings. The source codes of our method are available at: https://***/Nikphyc/SFDAH-MA. © 2025 IEEE.

关键词： Deep hashing image retrieval source-free domain adaptation

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Adaptive Early Warning Method of Cascading Failures Caused by Coordinated Cyber-Attacks

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CSEE Journal of power and Energy Systems 2025年第1期11卷 406-423页

作者： Yufei Wang June Li Jian Qiu Yangrong Chen Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of EducationSchool of Cyber Science and EngineeringWuhan UniversityWuhan Hubei430072China China Electric Power Research Institute Beijing102209China Electric Power Planning&Engineering Institute Beijing 100120China

In order to accurately receive early warning of the cascading failures caused by coordinated cyber-attacks(CFCC)in grid cyber-physical systems(GCPS),an adaptive early warning method of CFCC is ***,the evolutionary mechanism of CFCC is analyzed from the attackers'view,the CFCC mathematical model is established,and the transition processes of GCPS running states under the influence of CFCC staged failures are ***,the mathematical model of the adaptive early warning method is ***,the mathematical model of the adaptive early warning method is mapped as an adaptive control process with tolerating staged failures damage,and the solving process is presented to infer the CFCC and its next evolution trend.A decision-making idea for the optimal active defense scheme is proposed considering the costs and gains of various defense ***,to verify the effectiveness of the adaptive early warning method,the warning and defense processes of a typical CFCC are simulated in a GCPS experimental system based on CEPRI-36 BUS.

关键词： Adaptive control theory cascading failures coordinated cyber-attacks early warning grid cyber-physical systems

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network Intrusion Detection for Modern Smart Grids Based on Adaptive Online Incremental Learning

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IEEE Transactions on Smart Grid 2025年第3期16卷 2541-2553页

作者： Lu, Qiuyu An, Kexin Li, Jun'e Wang, Jin Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China State Grid Hubei Electric Power Research Institute Energy Internet Research Center Wuhan430072 China

New and evolving cyber attacks against smart grids are emerging. This necessitates that the network intrusion detection systems (IDSs) have online learning ability. However, most existing methods struggle to handle new and evolving attacks while retaining old attack knowledge, and many of them employ deep models requiring long update periods. Therefore, we propose an IDS based on adaptive online incremental learning (AdaOIL-IDS). 1) A class-correlated broad learning system (CC-BLS) is proposed for intrusion detection. A weighted CC-factor derived from intra- and inter-class correlations is introduced in CC-BLS to improve classification accuracy. CC incremental learnings are designed to quickly add new inputs and additional nodes without retraining. The CC-factor for new inputs is adjusted based on correlations of new and old classes, which enables simultaneous adaptation to new attacks and new observations of old attacks while retaining more old knowledge. 2) An adaptive learning framework is proposed for online offline combined learning of models. Online learning and offline retraining are adaptive switched based on the real-time loss to achieve efficient lifelong learning. Experiment results show that CC-BLS has better performance than selected state-of-the-art incremental broad and deep models, and the proposed adaptive learning framework behaviors better effectiveness and efficiency than selected existing frameworks. © 2010-2012 IEEE.

关键词： network intrusion

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