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20th International Conference on Information security and Cryptology, Inscrypt 2024

作者： Feng, Yansong Liu, Zhen Nitaj, Abderrahmane Pan, Yanbin State Key Laboratory of Mathematical Sciences Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China Hubei Provincial Engineering Research Center of Intelligent Connected Vehicle Network Security School of Cyber Science and Technology Hubei University Wuhan 430062 China Normandie Univ UNICAEN CNRS LMNO Caen 14000 France

ISBN: (纸本)9789819647330

At Asiacrypt’23, Meers and Nowakowski introduced a new automated method called Automated Coppersmith, which can be viewed as a generalization of Jochemsz-May Strategy for systems of polynomial equations. It selects monomials first, then constructs polynomials to construct the lattice in Coppersmith’s method. However, their strategy raises a new heuristic assumption. In this paper, We try to eliminate this heuristic assumption on linear equation systems. Finally, we apply this method to analyze the Generalized Extended Implicit Factorization Problem (G-EIFP), a generalization of the Extended Implicit Factorization Problem (EIFP), even when the shared contiguous bits are arbitrary. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Automated Coppersmith’s method Factorization Implicit Factorization Problem Lattice basis reduction RSA cryptosystem

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ATHENA: An In-vehicle CAN Intrusion Detection Framework Based on Physical Characteristics of Vehicle Systems

arXiv

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arXiv 2025年

作者： Wang, Kai Sun, Zhen Wang, Bailing Fan, Qilin Li, Ming Zhang, Hongke School of Computer Science and Technology Harbin Institute of Technology Weihai China Shandong Key Laboratory of Industrial Network Security China School of Big Data and Software Engineering Chongqing University Chongqing China Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing University Chongqing China Jinan Key Laboratory of Distributed Databases Shandong Inspur Database Technology Co. Ltd Jinan China School of Electronic and Information Engineering Beijing Jiaotong University Beijing China

With the growing interconnection between In-Vehicle networks (IVNs) and external environments, intelligent vehicles are increasingly vulnerable to sophisticated external network attacks. This paper proposes ATHENA, the first IVN intrusion detection framework that adopts a vehicle-cloud integrated architecture to achieve better security performance for the resource-constrained vehicular environment. Specifically, in the cloud with sufficient resources, ATHENA uses the clustering method of multi-distribution mixture model combined with deep data mining technology to generate the raw Payload Rule Bank of IVN CAN messages, and then improves the rule quality with the help of exploitation on the first-principled physical knowledge of the vehicle system, after which the payload rules are periodically sent to the vehicle terminal. At the vehicle terminal, a simple LSTM component is used to generate the Time Rule Bank representing the long-term time series dependencies and the periodic characteristics of CAN messages, but not for any detection tasks as in traditional usage scenarios, where only the generated time rules are the candidates for further IVN intrusion detection tasks. Based on both the payload and time rules generated from cloud and vehicle terminal, ATHENA can achieve efficient intrusion detection capability by simple rule-base matching operations, rather than using complex black-box reasoning of resource-intensive neural network models, which is in fact only used for rule logic generation phase instead of the actual intrusion detection phase in our framework. Comparative experimental results on the ROAD dataset, which is current the most outstanding real-world in-vehicle CAN dataset covering new instances of sophisticated and stealthy masquerade attacks, demonstrate ATHENA significantly outperforms the state-of-the-art IVN intrusion detection methods in detecting complex attacks. We make the code available at https://***/wangkai-tech23/ATHENA. Copyright

关键词： network intrusion

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RoPA: Robust Privacy-Preserving Forward Aggregation for Split Vertical Federated Learning

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IEEE Transactions on network and Service Management 2025年

作者： Wang, Lingling Zhang, Zhengyin Huang, Mei Gai, Keke Wang, Jingjing Shen, Yulong Qingdao University of Science and Technology School of Information Science and Technology Shandong Qingdao 266061 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing 100081 China Xidian University School of Computer Science and Technology China Shaanxi Key Laboratory of Network and System Security Xi’an 710071 China

Split Vertical Federated Learning (Split VFL) is an increasingly popular framework for collaborative machine learning on vertically partitioned data. However, it is vulnerable to various attacks, resulting in privacy leakage and robust aggregation issues. Recent works have explored the privacy protection of raw data samples and labels, neglecting malicious attacks launched by dishonest passive parties. Since they may deviate from the protocol and launch embedding poisoning attacks and free-riding attacks, it will inevitably result in model performance loss. To address this issue, we propose a Robust Privacy-preserving forward Aggregation (RoPA) protocol, which can resist embedding poisoning attacks and free-riding attacks and protect the privacy of embedding vectors. Specifically, we first present a modified Secret-shared Non-Interactive Proofs (SNIP) algorithm to guarantee the integrity verification of embedding vectors. To prevent free-riding attacks, we also give a validity verification protocol using matrix commitment. In particular, we utilize probability checking and batch verification to improve the verification efficiency of the protocol. Moreover, we adopt arithmetic secret sharing to protect data privacy. Finally, we conduct rigorous theoretical analysis to prove the security of RoPA and evaluate the performance of RoPA. The experimental results show that the proof verification overhead of RoPA is approximately 8× lower than the original SNIP, and the model accuracy is improved by ranging from 3% to 15% under the above two malicious attacks. © 2004-2012 IEEE.

关键词： Integrity Privacy-preserving Robust SNIP Vertical federated learning

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Privacy-Preserving IoT Image Transmission: Multi-Stage SVD Data Embedding and Heatmap Alignment

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IEEE Internet of Things Journal 2025年

作者： Du, Kaixin Ma, Bin Yang, Meihong Wang, Xiaoyu Li, Xiaolong Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250101 China Beijing Jiaotong University School of Computer and Information Technology Beijing100044 China

The images transmitted by IoT devices, particularly those used for surveillance or sensor data, are vulnerable to malicious screenshots and unauthorized access, leading to potential privacy breaches. To address this, we propose a multi-stage Singular Value Decomposition (SVD)-based robust data-hiding scheme for JPEG images aimed at mitigating screenshot attacks. The method exploits the decorrelation properties of the Discrete Cosine Transform (DCT) to preprocess the carrier image, facilitating the selection of specific frequency coefficients. These coefficients undergo a dual-stage SVD transformation, where dimensionality reduction reduces the impact of noise from non-critical image regions. Additionally, we optimize Grad-CAM heatmap generation to better align with human visual perception, enabling the identification of stable and reliable feature regions for embedding secret information. This approach ensures that the visual integrity of the carrier image is maintained while preserving the legibility of the embedded information, even under *** method enhances both the visual fidelity of the carrier image and the robustness of the embedded information. Experimental results demonstrate that the proposed scheme outperforms existing methods, achieving at least a 5% improvement in confidential information extraction accuracy and a data extraction rate exceeding 95% across screenshot angles ranging from -40∘ to 40∘. Extensive evaluations confirm the superior performance, efficiency, and security of our method in mitigating screenshot attacks, showcasing its broad applicability to various image formats and resilience to distortions. © 2014 IEEE.

关键词： Discrete cosine transforms

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Research on Loophole with Second Distribution of Real Value Detectors

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Chinese Journal of Electronics 2016年第6期25卷 1141-1150页

作者： WANG Tianbo ZHANG Fengbin XIA Chunhe Beijing Key Laboratory of Network Technology Beihang University Network and Information Security Laboratory Harbin University of Science and Technology

Traditional anomaly detection algorithm has improved to some degree the mechanism of negative selection. There still remain many problems such as the randomness of detector generation, incompleteness of selfset and the generalization ability of detectors, which would cause a lot of loopholes in non-self space. A heuristic algorithm based on the second distribution of real value detectors for the remains of loopholes of the non-self space in the first distribution and the mutation regions of self space is proposed. The algorithm can distribute real value detectors through omission data based on the methods of partition and movement. A method is proposed to solve the problem on how to get the optimal solutions to the parameters related in the algorithm. Theoretical analysis and experimental results prove the universality and effectiveness of the method. It is found that our algorithm can effectively avoid the generation of loopholes and thus reduce the omission rate of detector sets.

关键词： Artificial immune Anomaly detection Loophole Parameter estimation Detector

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CGNet: Classification-Guided Multi-Task Interactive network for Hyperspectral and Multispectral Image Fusion

CGNet: Classification-Guided Multi-Task Interactive Network ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Guohua Lv Yanlong Xu Yongbiao Gao Guixin Zhao Guotao Wang Xiangcheng Sun Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science China Shandong Key Laboratory of Ubiquitous Intelligent Computing China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Application (Southeast University) Ministry of Education China Beihang University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The goal of fusing hyperspectral images (HSI) and multispectral images (MSI) is to generate high-resolution hyperspectral images for downstream tasks. However, most existing methods overlook the specific requirements of these tasks, leading to a gap between the fusion process and its subsequent applications due to insufficient guidance from downstream tasks. To address this issue, we propose a classification-guided multitask interactive network (CGNet) that integrates both fusion and classification tasks into a unified framework, with two branches producing the fused image and classification results, respectively. In the fusion branch, we design a multi-level residual refinement module to efficiently integrate spatial and spectral information. Additionally, an attention-based multi-scale fusion module, incorporating both spatial and channel attention, is carefully crafted to enhance representation learning. In the classification branch, both 2-D and 3-D convolutions are employed to improve classification performance. Moreover, an information interaction module is proposed to guide the fusion task based on classification outcomes. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches on the Pavia Centre and Pavia University datasets.

关键词： Representation learning Three-dimensional displays Attention mechanisms Convolution Streaming media Multitasking Speech processing Image fusion Information exchange Hyperspectral imaging

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Maximizing Area Coverage in Privacy-Preserving Worker Recruitment: A Prior Knowledge-Enhanced Geo-indistinguishable Approach

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IEEE Transactions on Information Forensics and security 2025年 20卷 5138-5151页

作者： Zhang, Pengfei Cheng, Xiang Zhang, Zhikun Zhu, Youwen Zhang, Ji Anhui University of Science and Technology State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining the School of Computer Science and Engineering Anhui University of Science and Technology Huainan 232001 China Yunnan University of Finance and Economics Yunnan Key Laboratory of Service Computing Yunnan 650221 China Qilu University of Technology (Shandong Academy of Sciences) Key Laboratory of Equipment Data Security and Guarantee Technology Ministry of Education Key Laboratory of Computing Power Network and Information Security Ministry of Education China Beijing University of Posts and Telecommunications China Zhejiang University China Nanjing University of Aeronautics and Astronautics China University of Southern Queensland China

Worker recruitment for area coverage maximization, typically requires participants to upload location information, which can deter potential participation without proper protection. While existing studies resort to geo-indistinguishability to address this concern, they primarily focus on either specific task locations (Target Coverage) or operate under pre-defined recruitment quotas for an interested region (Area Coverage). These focuses not only yield suboptimal area coverage when scaled but also fail to leverage valuable prior knowledge in the form of participants’ noisy historical registered locations, to enhance both location obfuscation and worker identification processes. To address these limitations, we present WILTON, which optimizes area coverage under geo-indistinguishability by recruiting the minimum number of participants through the strategic utilization of noisy prior knowledge. In WILTON, to generate obfuscated locations, we propose a probabilistic and weight-aware input perturbation mechanism, which groups and weights prior locations rather than using only personal prior locations. To privately identify the recruited workers, we design a grid-based worker identification method, which provides a worst-case performance guarantee of ratio (Formula Presented) to the optimum. We provide a theoretical analysis of the privacy, utility, and complexity guarantees of WILTON. Experimental results over two real-world datasets and one synthetic dataset show that WILTON surpasses the state-of-the-arts by at least 8% in area coverage improvement. © 2005-2012 IEEE.

关键词： Geo-indistinguishability Prior Knowledge Spatial Crowdsourcing Worker Recruitment

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Cross-Layer security for Semantic Communications: Metrics and Optimization

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IEEE Transactions on Vehicular technology 2025年

作者： Wang, Lingyi Wu, Wei Zhou, Fuhui Qin, Zhijin Wu, Qihui Nanjing University of Posts and Telecommunications College of Science Nanjing210003 China Nanjing University of Posts and Telecommunications College of Communication and Information Engineering Nanjing210003 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing210000 China Tsinghua University Department of Electronic Engineering Beijing100084 China Beijing National Research Center for Information Science and Technology Beijing China State Key Laboratory of Space Network and Communications Beijing China

Different from traditional secure communication that focuses on symbolic protection at the physical layer, semantic secure communication requires further attention to semantic-level task performance at the application layer. There is a research gap on how to comprehensively evaluate and optimize the security performance of semantic communication. In order to fill this gap, a unified semantic security metric, the cross-layer semantic secure rate (CLSSR), is defined to estimate cross-layer security requirements at both the physical layer and the application layer. Then, we formulate the maximization problem of the CLSSR with the mixed integer nonlinear programming (MINLP). We propose a hierarchical AI-native semantic secure communication network with a reinforcement learning (RL)-based semantic resource allocation scheme, aiming to ensure the cross-layer semantic security (CL-SS). Finally, we prove the convergence of our proposed intelligent resource allocation, and the simulation results demonstrate that our proposed CLSS method outperforms the traditional physical layer semantic security (PL-SS) method in terms of both task reliability and CLSSR. © 1967-2012 IEEE.

关键词： Reinforcement learning

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Full-scale studies are closer to reality than lab-scale ones: Tertiary treatment constructed wetlands maintain robust removal capability for pharmaceuticals and personal care products in cold weather conditions

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Bioresource technology 2025年 432卷 132661页

作者： Wang, Jiaxi Ye, Beibei Yu, Xia Yan, Ruiqi Lin, Hui Man, Ying Sui, Qian State Environmental Protection Key Laboratory of Environmental Risk Assessment and Control on Chemical Process School of Resources and Environmental Engineering East China University of Science and Technology Shanghai200237 China Beijing100015 China School of Chemistry and Environment Guangdong Ocean University Zhanjiang524088 China Shanghai Institute of Pollution Control and Ecological Security Shanghai200092 China

In contrast to previous lab-scale studies, full-scale studies show that low temperatures do not significantly hinder pharmaceutical and personal care product (PPCP) removal in tertiary treatment constructed wetland (CW) systems. This study investigated PPCP removal pathways (substrate adsorption, plant uptake, microbial degradation) in two full-scale CWs, highlighting the differences between full-scale and lab-scale study outcomes. Results demonstrated that low temperatures did not adversely affect PPCP removal in full-scale CWs. Substrate adsorption was minimally impacted at a relevant concentration of 100 ng/L, unlike lab-scale studies with typically higher concentrations (1 mg/L). Plant uptake was not affected, as mature plants in CWs exhibited a lower PPCP uptake capacity compared to seedlings in lab-scale studies. Furthermore, low temperatures did not reduce microbial biodegradation performance. However, low temperatures increased dissolved oxygen concentrations in the influent, supporting aerobic microbial activity. These findings emphasize the value of full-scale CWs for stable PPCP removal under low-temperature conditions. © 2025

关键词： Inclusions

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A Grouping Strategy-Based Progressive Fusion network for Hyperspectral Image Super-Resolution

A Grouping Strategy-Based Progressive Fusion Network for Hyp...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Guohua Lv Baodong Zhang Yongbiao Gao Guixin Zhao Guotao Wang Juncan Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Shandong Key Laboratory of Ubiquitous Intelligent Computing Jinan China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Application (Southeast University) Ministry of Education China Beihang University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Hyperspectral super-resolution involves combining low-resolution hyperspectral images with high-resolution multispectral images to produce a high-resolution hyperspectral image. Recently, although many methods for hyperspectral image super-resolution have been proposed, they often fail to fully utilize the high similarity among adjacent bands to enhance fusion performance. Therefore, we propose a grouping strategy-based progressive fusion network (GPFNet) for hyperspectral super-resolution. The core of GPFNet is the grouping strategy fusion block (GPF block), in which grouping-based spatial-spectral information fusion and spatial information refinement are performed. We design the spatial-spectral information fusion module (SSIFM) based on grouped convolutions to capture the feature differences from adjacent bands. To refine spatial details, we develop the spatial information enhancement module (SpaEM), which leverages the hierarchical features extracted by the multi-scale feature extraction module (MIEM). Additionally, a progressive fusion strategy, which involves using multiple upsampled hyperspectral images and downsampled multispectral images, further preserves spectral integrity and spatial details. Extensive experiments show that GPFNet outperforms state-of-the-art methods both qualitatively and quantitatively.

关键词： Convolution Superresolution Feature extraction Acoustics Data mining Speech processing Hyperspectral imaging

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