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Full-round impossible diferential attack on shadow block cipher

Cybersecurity 2025年第1期8卷 247-261页

作者： Yuting Liu Yongqiang Li Huiqin Chen Mingsheng Wang State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of SciencesBeijingChina School of Cyber Security University of Chinese Academy of SciencesBeijingChina

Lightweight block ciphers are the essential encryption algorithm for devices with limited *** goal is to ensure the security of data transmission through resource-constrained *** diferential cryptanalysis is one of the most efective cryptanalysis on block ciphers,and assessing the ability of resisting this attack is a basic design *** is a lightweight block cipher proposed by Guo et al.(IEEE Internet Things J 8(16):13014-13023,2021).It utilizes a combination of ARX operations and generalized Feistel structure to overcome the weakness of the traditional Feistel structure that only difuses half in one *** this paper,we focus on the differential property of Shadow and its security against impossible diferential ***,we use the SAT method to automatically search for a full-round impossible diferential distinguisher of ***,based on the experimental results,we prove that Shadow has a diferential property with probability 1 based on the propagation of the ***,we can obtain an impossible diferential distinguisher for an arbitrary number of rounds of ***,we perform a full key recovery attack on the full-round Shadow-32 and *** experimentally and theoretically,our results indicate that Shadow is critically fawed,and regardless of the security strength of the internal components and the number of rounds applied,the overall cipher remains vulnerable to impossible diferential cryptanalysis.

关键词： Lightweight block cipher Shadow Impossible diferential cryptanalysis SAT

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Malicious Document Detection Based on GGE Visualization

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Computers, Materials & Continua 2025年第1期82卷 1233-1254页

作者： Youhe Wang Yi Sun Yujie Li Chuanqi Zhou Henan Province Key Laboratory of Information Security Information Engineering UniversityZhengzhou450000China

With the development of anti-virus technology,malicious documents have gradually become the main pathway of Advanced Persistent Threat(APT)attacks,therefore,the development of effective malicious document classifiers has become particularly ***,detection methods based on document structure and behavioral features encounter challenges in feature engineering,these methods not only have limited accuracy,but also consume large resources,and usually can only detect documents in specific formats,which lacks versatility and *** address such problems,this paper proposes a novel malicious document detection method-visualizing documents as GGE images(Grayscale,Grayscale matrix,Entropy).The GGE method visualizes the original byte sequence of the malicious document as a grayscale image,the information entropy sequence of the document as an entropy image,and at the same time,the grayscale level co-occurrence matrix and the texture and spatial information stored in it are converted into grayscale matrix image,and fuses the three types of images to get the GGE color *** Convolutional Block Attention Module-EfficientNet-B0(CBAM-EfficientNet-B0)model is then used for classification,combining transfer learning and applying the pre-trained model on the ImageNet dataset to the feature extraction process of GGE *** shown in the experimental results,the GGE method has superior performance compared with other methods,which is suitable for detecting malicious documents in different formats,and achieves an accuracy of 99.44%and 97.39%on Portable Document Format(PDF)and office datasets,respectively,and consumes less time during the detection process,which can be effectively applied to the task of detecting malicious documents in real-time.

关键词： Malicious document visualization EfficientNet-B0 convolutional block attention module GGE image

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Institution Attribute Mining Technology for Access Control Based on Hybrid Capsule Network

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Computers, Materials & Continua 2025年第4期83卷 1495-1513页

作者： Aodi Liu Xuehui Du Na Wang Xiangyu Wu Henan Province Key Laboratory of Information Security Information Engineering UniversityZhengzhou450000China

security attributes are the premise and foundation for implementing Attribute-Based Access Control(ABAC)***,when dealing with massive volumes of unstructured text big data resources,the current attribute management methods based on manual extraction face several issues,such as high costs for attribute extraction,long processing times,unstable accuracy,and poor *** address these problems,this paper proposes an attribute mining technology for access control institutions based on hybrid capsule *** technology leverages transfer learning ideas,utilizing Bidirectional Encoder Representations from Transformers(BERT)pre-trained language models to achieve vectorization of unstructured text data ***,we have designed a novel end-to-end parallel hybrid network structure,where the parallel networks handle global and local information features of the text that they excel at,*** employing techniques such as attention mechanisms,capsule networks,and dynamic routing,effective mining of security attributes for access control resources has been ***,we evaluated the performance level of the proposed attribute mining method for access control institutions through experiments on the medical referral text resource *** experimental results show that,compared with baseline algorithms,our method adopts a parallel network structure that can better balance global and local feature information,resulting in improved overall ***,it achieves a comprehensive performance enhancement of 2.06%to 8.18%in the F1 score ***,this technology can effectively provide attribute support for access control of unstructured text big data resources.

关键词： Access control ABAC model attribute mining capsule network deep learning

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BRITD:behavior rhythm insider threat detection with time awareness and user adaptation

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Cybersecurity 2025年第1期8卷 227-246页

作者： Shuang Song Neng Gao Yifei Zhang Cunqing Ma Institute of Information Engineering Chinese Academy of SciencesBeijingChina State Key of Laboratory of Information Security Chinese Academy of SciencesBeijingChina School of Cyber Security University of Chinese Academy of SciencesBeijingChina

Researchers usually detect insider threats by analyzing user *** time information of user behavior is an important concern in internal threat *** works on insider threat detection fail to make full use of the time information,which leads to their poor detection *** this paper,we propose a novel behavioral feature extraction scheme:we implicitly encode absolute time information in the behavioral feature sequences and use a feature sequence construction method taking covariance into account to make our scheme adaptive to *** select Stacked Bidirectional LSTM and Feedforward Neural Network to build a deep learning-based insider threat detection model:Behavior Rhythm Insider Threat Detection(BRITD).BRITD is universally applicable to various insider threat scenarios,and it has good insider threat detection performance:it achieves an AUC of 0.9730 and a precision of 0.8072 with the CMU CERT dataset,which exceeds all baselines.

关键词： Insider threat detection Behavior pattern mining Time information User adaptive Deep learning

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Minimizing CNOT-count in quantum circuit of the extended Shor’s algorithm for ECDLP

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Cybersecurity 2025年第1期8卷 153-179页

作者： Xia Liu Huan Yang Li Yang State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of SciencesBeijingChina School of Cyber Security University of Chinese Academy of SciencesBeijingChina Beijing Youzhuju Network Technology Co. Ltd.BeijingChina

The elliptic curve discrete logarithm problem(ECDLP)is a popular choice for cryptosystems due to its high level of ***,with the advent of the extended Shor’s algorithm,there is concern that ECDLP may soon be *** the algorithm does ofer hope in solving ECDLP,it is still uncertain whether it can pose a real threat in *** the perspective of the quantum circuits of the algorithm,this paper analyzes the feasibility of cracking ECDLP using an ion trap quantum computer with improved quantum circuits for the extended Shor’s *** give precise quantum circuits for extended Shor’s algorithm to calculate discrete logarithms on elliptic curves over prime felds,including modular subtraction,three diferent modular multiplication,and modular ***,we incorporate and improve upon windowed arithmetic in the circuits to reduce the *** previous studies mostly focused on minimizing the number of qubits or the depth of the circuit,we focus on minimizing the number of CNOT gates in the circuit,which greatly afects the running time of the algorithm on an ion trap quantum ***,we begin by presenting implementations of basic arithmetic operations with the lowest known CNOT-counts,along with improved constructions for modular inverse,point addition,and windowed ***,we precisely estimate that,to execute the extended Shor’s algorithm with the improved circuits to factor an n-bit integer,the CNOT-count required is1237n^(3)/log n+2n^(2)+***,we analyze the running time and feasibility of the extended Shor’s algorithm on an ion trap quantum computer.

关键词： Elliptic curve discrete logarithm problem Extended Shor’s algorithm Quantum circuits Ion trap quantum computer

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An Enhanced Clustering-Based (k, t)-Anonymity Algorithm for Graphs

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Chinese Journal of Electronics 2025年第1期34卷 365-372页

作者： Yuanyuan Wang Xing Zhang Zhiguang Chu Wei Shi Xiang Li School of Electronics and Information Engineering Liaoning University of Technology Key Laboratory of Security for Network and Data in Industrial Internet of Liaoning Province Faculty of Information Technology Beijing University of Technology

As people become increasingly reliant on the Internet, securely storing and publishing private data has become an important issue. In real life, the release of graph data can lead to privacy breaches, which is a highly challenging problem. Although current research has addressed the issue of identity disclosure, there are still two challenges: First, the privacy protection for large-scale datasets is not yet comprehensive; Second, it is difficult to simultaneously protect the privacy of nodes, edges, and attributes in social networks. To address these issues, this paper proposes a(k,t)-graph anonymity algorithm based on enhanced clustering. The algorithm uses k-means++ clustering for k-anonymity and t-closeness to improve k-anonymity. We evaluate the privacy and efficiency of this method on two datasets and achieved good results. This research is of great significance for addressing the problem of privacy breaches that may arise from the publication of graph data.

关键词： Data privacy Uncertainty Social networking (online) Publishing Heuristic algorithms Scalability Clustering algorithms Privacy breach Internet Protection

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SeaFlame: Communication-Efficient Secure Aggregation for Federated Learning against Malicious Entities

IACR Transactions on Cryptographic Hardware and Embedded Sys...

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IACR Transactions on Cryptographic Hardware and Embedded Systems 2025年第2期2025卷 69-93页

作者： Tang, Jinling Xu, Haixia Liao, Huimei Zhou, Yinchang Key Laboratory of Cyberspace Security Defense Institute of Information Engineering CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

Secure aggregation is a popular solution to ensuring privacy for federated learning. However, when considering malicious participants in secure aggregation, it is difficult to achieve both privacy and high efficiency. Therefore, we propose SeaFlame, a communication-efficient secure aggregation protocol against malicious participants. Inspired by the state-of-the-art work, ELSA, SeaFlame also utilizes two non-colluding servers to ensure privacy against malicious entities and provide defenses against boosted gradients. Crucially, to improve communication efficiency, SeaFlame uses arithmetic sharing together with arithmetic-to-arithmetic share conversion to reduce client communication, and uses the random linear combination to reduce server communication. security analysis proves that our SeaFlame guarantees privacy against malicious clients colluding with one malicious server. Experimental evaluation demonstrates that, compared to ELSA, SeaFlame optimizes communication by up to 10.5, 6.00, and 8.17 times for a client, a server, and all entities, at the expense of 1.25-1.86 times additional end-to-end runtime. © 2025, Ruhr-University of Bochum. All rights reserved.

关键词： Federated learning

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High Capacity Reversible Data Hiding Algorithm in Encrypted Images Based on Image Adaptive MSB Prediction and Secret Sharing

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1139-1156页

作者： Kaili Qi Minqing Zhang Fuqiang Di Chao Jiang Key Laboratory of Network & Information Security under PAP Engineering University of PAPXi'an 710086China

Until now,some reversible data hiding in encrypted images(RDH-EI)schemes based on secret sharing(SIS-RDHEI)still have the problems of not realizing diffusivity and high embedding ***,this paper innovatively proposes a high capacity RDH-EI scheme that combines adaptive most significant bit(MSB)prediction with secret sharing ***,adaptive MSB prediction is performed on the original image and cryptographic feedback secret sharing strategy encrypts the spliced pixels to spare embedding *** the data hiding phase,each encrypted image is sent to a data hider to embed the secret information *** r copies of the image carrying the secret text are collected,the original image can be recovered lossless and the secret information can be *** evaluation shows that the proposed method in this paper has the diffusivity,reversibility,and *** last but the most important,it has higher embedding *** 512 ×512 grayscale images,the average embedding rate reaches 4.7358 bits per pixel(bpp).Compared to the average embedding rate that can be achieved by the Wang et al.'s SIS-RDHEI scheme,the proposed scheme with(2,2),(2,3),(2,4),(3,4),and(3,5)-threshold can increase by 0.7358 bpp,2.0658 bpp,2.7358 bpp,0.7358 bpp,and 1.5358 bpp,respectively.

关键词： adaptive most significant bit(MSB)prediction password feedback secret sharing high embedding capacity reversible data hiding in encrypted images

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On-orbit attitude planning for Earth observation imaging with star-based geometric calibration

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Science China(Technological Sciences) 2025年第2期68卷 243-256页

作者： Di HUANG Zhifu WANG Jianya GONG Haoran JI Institute of Aerospace Science and Technology School of Remote Sensing and Information EngineeringWuhan UniversityWuhan 430072China State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingSchool of Remote Sensing and Information EngineeringWuhan UniversityWuhan 430072China National Key Laboratory of Air-based Information Perception and Fusion Luoyang 471009China Intelligent Game and Decision Laboratory Beijing 100071China

To improve the quality of remote sensing images,a novel on-orbit attitude planning method for Earth observation imaging with star-based geometric calibration is presented in this *** traditional imaging processes,the proposed method includes both pre-calibration and post-calibration stages to enhance the accuracy of satellite geometric positioning.A multiple-constraint attitude planning algorithm was developed to ensure star-based calibration,wherein the satellite camera and star sensors simultaneously capture the images of *** integration of cameras and multiple star sensors enables effective attitude *** the Earth imaging stage,a fast on-orbit attitude planning algorithm was developed to determine the satellite attitude and the optimal imaging times for ground *** with existing methods,the fast on-orbit attitude planning algorithm can significantly reduce computational time and resource consumption via initial value selection and *** demonstrate the effectiveness of the proposed attitude planning method,which was successfully applied to Wuhan 1 satellite.

关键词： attitude planning Earth observation imaging geometric calibration attitude constraints

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Fairness is essential for robustness:fair adversarial training by identifying and augmenting hard examples

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Frontiers of Computer Science 2025年第3期19卷 1-13页

作者： Ningping MOU Xinli YUE Lingchen ZHAO Qian WANG Key Laboratory of Aerospace Information Security and Trusted Computing(Ministry of Education) School of Cyber Science and EngineeringWuhan UniversityWuhan 430072China

Adversarial training has been widely considered the most effective defense against adversarial ***,recent studies have demonstrated that a large discrepancy exists in the class-wise robustness of adversarial training,leading to two potential issues:firstly,the overall robustness of a model is compromised due to the weakest class;and secondly,ethical concerns arising from unequal protection and biases,where certain societal demographic groups receive less robustness in defense *** these issues,solutions to address the discrepancy remain largely *** this paper,we advance beyond existing methods that focus on class-level *** investigation reveals that hard examples,identified by higher cross-entropy values,can provide more fine-grained information about the ***,we find that enhancing the diversity of hard examples can effectively reduce the robustness gap between *** by these observations,we propose Fair Adversarial Training(FairAT)to mitigate the discrepancy of class-wise *** experiments on various benchmark datasets and adversarial attacks demonstrate that FairAT outperforms state-of-the-art methods in terms of both overall robustness and *** a WRN-28-10 model trained on CIFAR10,FairAT improves the average and worst-class robustness by 2.13%and 4.50%,respectively.

关键词： robust fairness adversarial training hard example data augmentation

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