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Extracting hidden messages of MLSB steganography based on optimal stego subset

Science China(Information Sciences) 2018年第11期61卷 241-243页

作者： Chunfang YANG Xiangyang LUO Jicang LU Fenlin LIU Zhengzhou Information Science and Technology Institute State Key Laboratory of Mathematical Engineering and Advanced Computing

Dear editor,In recent years, steganalysis researchers have tried their best to extract the hidden messages. For example, under the condition of a known embedding position generator, Liu et al.[1] proposed a collision attack algorithm to recover the stego key of least significant bit (LSB) steganography. Fridrich et al.[2] proposed a chi-squared-test-based method to recover the stego key of LSB steganography for the case of an unknown carrier. Under the condition of multiple stego images embedded into the same positions, Ker [3] first proposed locating the

关键词： LSB

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On s-uniform property of compressing sequences derived from primitive sequences modulo odd prime powers

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Science China(Information Sciences) 2017年第5期60卷 84-95页

作者： Yupeng JIANG Qun-Xiong ZHENG Dongdai LIN State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of Sciences State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Information Science and Technology Institute

Let Z/（p;） be the integer residue ring modulo pewith p an odd prime and e 2. We consider the suniform property of compressing sequences derived from primitive sequences over Z/（p;）. We give necessary and sufficient conditions for two compressing sequences to be s-uniform with α provided that the compressing map is of the form φ(x;, x;,..., x;) = g(x;) + η(x;, x;,..., x;), where g(x;) is a permutation polynomial over Z/（p） and η is an（e-1）-variable polynomial over Z/（p）.

关键词： compressing map linear recurring sequence primitive sequence permutation polynomial s uniform

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Continual Reinforcement Learning for Digital Twin Synchronization Optimization

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IEEE Transactions on Mobile computing 2025年

作者： Tong, Haonan Chen, Mingzhe Zhao, Jun Hu, Ye Yang, Zhaohui Liu, Yuchen Yin, Changchuan Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing Advanced Information Network Laboratory Beijing China College of Computing and Data Science Singapore University of Miami Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing Coral Gables33146 United States University of Miami Department of Industrial and Systems Engineering Coral Gables33146 United States Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China North Carolina State University Department of Computer Science RaleighNC27695 United States Beijing Key Laboratory of Network System Architecture and Convergence China Beijing University of Posts and Telecommunications Beijing Advanced Information Network Laboratory Beijing100876 China

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods. © 2025 IEEE. All rights reserved.

关键词： Markov processes

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Improvement in Diversify Active Defense for Web Application by Using Language and Database Heterogeneity 11

Improvement in Diversify Active Defense for Web Application ...

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2017 11th IEEE International Conference on Anti-counterfeiting,Security,and Identification(ASID)

作者： Weichao Li Zheng Zhang Liqun Wang State Key Laboratory of Mathematical Engineering and Advanced Computing

ISBN: (纸本)9781538605349;9781538605332

According to OWASP selected web application Top 10 vulnerabilities in 2013 and 2017,structured query language(SQL)injection is consistently ranked the 1 ***,the protection of SQL injection,which is one of the most prevalent and pernicious security issues,requires ***,there are numerous unknown vulnerabilities and potential threats in *** this case,the active defense based on structural diversity can play an effective role to prevent the hacker from exploiting known or unknown *** on the basis of structural diversity,we propose a modified method,heterogeneous language,combined with heterogeneous database in data storage layer,to establish an active defense model for data *** empirically assess the impact of the vulnerability and conclude by testing the accuracy and performance,showing that our security model can not suffer from the same vulnerability as the unprotected ***,the future work and research direction are discussed.

关键词： Cyberspace security Active defence Heterogeneity Web application Database SQL labeling

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Image Classification for Malware Detection using Extremely Randomized Trees 11

Image Classification for Malware Detection using Extremely R...

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2017 11th IEEE International Conference on Anti-counterfeiting,Security,and Identification(ASID)

作者： Xin Zhou Jianmin Pang Guanghui Liang State Key Laboratory of Mathematical Engineering and Advanced Computing

ISBN: (纸本)9781538605349;9781538605332

With the development of machine learning and computer vision, we combined the image classification and malware detection in this work. Based on the technology of image classification, we achieved the detection of malware, using machine learning. In our work, we visualized the malware as a grayscale image and extracted texture features by Gabor filter. Depend on machine learning, we used extremely randomized trees as the classification and 10-fold cross validation to value it. Compared with GBDT, KNN and RF, experimental results are quite impressive with the accuracy rate of 96.19% and the recall rate of 97.51% on a malware database of 15,781 samples.

关键词： component malware image classification machine learning extremely randomized trees

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Research and Implementation of Reconfigurable Architecture of DES and ZUC 2

Research and Implementation of Reconfigurable Architecture o...

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2017 IEEE 第2届先进信息技术,电子与自动化控制国际会议(IAEAC 2017)

作者： Yuhan Chen Xuehui Du Wei Xiao Haiyang Zhang State Key Laboratory of Mathematical Engineering and Advanced Computing

Reconfigurable cipher chip has become a research hotspot because of its flexibility, security and good resource utilization. At present, research of reconfigurable cipher chip usually focuses on the s

ISBN: (纸本)9781467389808

关键词： Erbium

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IP-Pealing: A Robust Network Flow Watermarking Method Based on IP Packet Sequence

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Chinese Journal of Electronics 2024年第3期33卷 694-707页

作者： Wangxin FENG Xiangyang LUO Tengyao LI Chunfang YANG State Key Laboratory of Mathematical Engineering and Advanced Computing Key Laboratory of Cyberspace Situation Awareness of Henan Province

Network flow watermarking(NFW) is usually used for flow *** actively modulating some features of the carrier traffic,NFW can establish the correspondence between different network *** the face of strict demands of network traffic tracing,current watermarking methods cannot work efficiently due to the dependence on specific protocols,demand for large quantities of packets,weakness on resisting network channel interferences and so *** this end,we propose a robust network flow watermarking method based on IP packet sequence,called as *** is designed to utilize the packet sequence as watermark carrier with IP identification field which is insensitive to time jitter and suitable for all IP based *** enhance the robustness against packet loss and packet reordering,the detection sequence set is constructed in terms of the variation range of packet sequence,correcting the possible errors caused by the network *** improve the detection accuracy,the long watermark information is divided into several short sequences to embed in turn and assembled during *** a large number of experiments on the Internet,the overall detection rate and accuracy of IP-Pealing reach 99.91% and 99.42%*** comparison with the classical network flow watermarking methods,such as PROFW,IBW,ICBW,WBIPD and SBTT,the accuracy of IP-Pealing is increased by 13.70% to 54.00%.

关键词： Network flow watermarking IP packet sequence Detection sequence set Fast detection Robustness

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A Note on Determine the Greatest Common Subfamily of Two NFSRs by Grbner Basis

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Journal of Systems Science & Complexity 2015年第5期28卷 1231-1242页

作者： WANG Zhongxiao QI Wenfeng TIAN Tian Luoyang University of Foreign Language State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Information Science and Technology Institute

For nonlinear feedback shift registers(NFSRs), their greatest common subfamily may be not unique. Given two NFSRs, the authors only consider the case that their greatest common subfamily exists and is unique. If the greatest common subfamily is exactly the set of all sequences which can be generated by both of them, the authors can determine it by Grbner basis theory. Otherwise, the authors can determine it under some conditions and partly solve the problem.

关键词： Greatest common subfamily Grbner basis nonlinear feedback shift register stream cipher

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Visual Class Incremental Learning with Textual Priors Guidance based on an Adapted Vision-Language Model

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IEEE Transactions on Multimedia 2025年

作者： Zhang, Wentao Yu, Tong Wang, Ruixuan Xie, Jianhui Trucco, Emanuele Zheng, Wei-Shi Yang, Xiaobo Sun Yat-sen Univerisity School of Computer Science and Engineering Guangzhou510275 China Peng Cheng Laboratory Shenzhen518066 China MOE Key Laboratory of Machine Intelligence and Advanced Computing Guangzhou510275 China Second Affiliated Hospital Guangzhou University of Chinese Medicine State Key Laboratory of Dampness Syndrome of Chinese Medicine Guangzhou510260 China Guangdong Provincial Key Laboratory of Clinical Research on Traditional Chinese Medicine Syndrome Guangzhou510120 China University of Dundee School of Science and Engineering DundeeDD1 4HN United Kingdom

An ideal artificial intelligence (AI) system should have the capability to continually learn like humans. However, when learning new knowledge, AI systems often suffer from catastrophic forgetting of old knowledge. Although many continual learning methods have been proposed, they often ignore the issue of misclassifying similar classes and make insufficient use of textual priors of visual classes to improve continual learning performance. In this study, we propose a continual learning framework based on a pre-trained vision-language model (VLM) that does not require storing old class data. This framework utilizes parameter-efficient fine-tuning of the VLM's text encoder for constructing a shared and consistent semantic textual space throughout the continual learning process. The textual priors of visual classes are encoded by the adapted VLM's text encoder to generate discriminative semantic representations, which are then used to guide the learning of visual classes. Additionally, fake out-of-distribution (OOD) images constructed from each training image further assist in the learning of visual classes. Extensive empirical evaluations on three natural datasets and one medical dataset demonstrate the superiority of the proposed framework. The source code is available at https://***/OpenMedIA/CIL_Adapterd_VLM. © 1999-2012 IEEE.

关键词： Contrastive Learning

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IP2vec:an IP node representation model for IP geolocation

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Frontiers of Computer Science 2024年第6期18卷 189-204页

作者： Fan ZHANG Meijuan YIN Fenlin LIU Xiangyang LUO Shuodi ZU State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou 450001China Key Laboratory of Cyberspace Situation Awareness of Henan Province Zhengzhou 450001China

IP geolocation is essential for the territorial analysis of sensitive network entities,location-based services(LBS)and network fraud *** has important theoretical significance and application ***-based IP geolocation is a hot research ***,the existing IP geolocation algorithms cannot effectively utilize the distance characteristics of the delay,and the nodes’connection relation,resulting in high geolocation *** is challenging to obtain the mapping between delay,nodes’connection relation,and geographical *** on the idea of network representation learning,we propose a representation learning model for IP nodes(IP2vec for short)and apply it to street-level IP ***2vec model vectorizes nodes according to the connection relation and delay between nodes so that the IP vectors can reflect the distance and topological proximity between IP *** steps of the street-level IP geolocation algorithm based on IP2vec model are as follows:Firstly,we measure landmarks and target IP to obtain delay and path information to construct the network ***,we use the IP2vec model to obtain the IP vectors from the network ***,we train a neural network to fit the mapping relation between vectors and locations of ***,the vector of target IP is fed into the neural network to obtain the geographical location of target *** algorithm can accurately infer geographical locations of target IPs based on delay and topological proximity embedded in the IP *** cross-validation experimental results on 10023 target IPs in New York,Beijing,Hong Kong,and Zhengzhou demonstrate that the proposed algorithm can achieve street-level *** with the existing algorithms such as Hop-Hot,IP-geolocater and SLG,the mean geolocation error of the proposed algorithm is reduced by 33%,39%,and 51%,respectively.

关键词： IP geolocation network measurement node embedding

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