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Iterative Bounded Distance Decoding With Random Flipping for Product-Like Codes

IEEE Transactions on Communications 2025年第5期73卷 2864-2875页

作者： Li, Guorong Wang, Shiguo Zhao, Shancheng Jinan University National and Local Joint Research Center of Network Security Detection and Protection Technology College of Information Science and Technology Guangzhou510632 China Guangdong Key Laboratory of Data Security and Privacy Preserving Guangzhou Key Laboratory of Data Security and Privacy Preserving Guangzhou510632 China Changsha University of Science and Technology Computer and Communication Engineering Institute Changsha410114 China

Product-like codes are widely used in high-speed communication systems since they can be decoded with low-complexity hard decision decoders (HDDs). To meet the growing demand of data rates, enhanced HDDs are required. In this paper, we propose a novel soft-aided HDD (SA-HDD), termed iterative bounded distance decoding with random flipping (iBDD-RF), for product-like codes. In iBDD-RF, the soft reliability of a bit is a weighted sum of the output of bounded distance decoder (BDD) and the channel log-likelihood ratio (LLR). When the amplitude of the soft reliability of a bit is less than a given threshold, it is flipped with a given probability. This random flipping may make the decoder escape from the local optimum. To optimize the threshold and the flipping probability, we derive the density evolution (DE) equations of iBDD-RF for product codes (PCs) and staircase codes (SCs). Our extensive numerical results show that iBDD-RF outperforms iBDD with scaled reliability (iBDD-SR) over the binary-input additive white Gaussian noise (Bi-AWGN) channel. Particularly, for a PC with (255,239,2) Bose-Chaudhuri-Hocquenghem (BCH) code and an SC with (254,230,3) BCH code, iBDD-RF performs about 0.25 dB and 0.28 dB better than iBDD-SR, respectively. © 1972-2012 IEEE.

关键词： Optical fiber communication

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Spectrum fragmentation-aware dynamic network slicing deployment in computing power networks based on elastic optical networks

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Journal of Optical Communications 2025年第2期46卷 335-346页

作者： Wang, Laiming Zhang, Haojie Li, Lei Ren, Danping Hu, Jinhua Zhao, Jijun School of Information and Electrical Engineering Hebei University of Engineering Handan056038 China Hebei Key Laboratory of Security and Protection Information Sensing and Processing Handan056038 China Beijing KELIBANG Information Technology Corporation Limited Beijing10081 China

The widespread application of AI with high computing requirements has driven the rapid development of the computing field. Computing Power Networks (CPNs) have been recognized as solutions to providing on-demand computing services, and its service provisioning can be modeled as a network slicing deployment problem. Elastic Optical Networks (EONs) offer the flexibility to allocate spectrum resources, making them well-suited for network slicing technology. Consequently, EON-based CPNs have attracted considerable attention. However, the unbalanced distribution of computing resources leads to inefficient computing resource utilization. Meanwhile, spectrum resources may be isolated and difficult for other services. This phenomenon is known as spectrum fragmentation, leading to inefficient spectrum resource utilization. To achieve balanced and efficient resource utilization, this paper first analyzes the main reasons for load unbalance and spectrum fragmentation in CPNs: mismatched slicing deployment and inappropriate resource scheduling. Therefore, a dynamic network slicing scheme based on traffic prediction (DNS-TP) is designed. Its core highlight is cooperative optimization slicing deployment and resource scheduling based on spectrum fragmentation awareness. Simulation results show that the proposed scheme enhances the network slicing acceptance ratio, computing and spectrum resource utilization while exhibiting strong performance in resource balancing. © 2024 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Fiber optic networks

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Fine-Grained Zero-Shot Object Detection via Semantic Decoupling 24

Fine-Grained Zero-Shot Object Detection via Semantic Decoupl...

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8th International Conference on Computer Science and Artificial Intelligence, CSAI 2024

作者： Duan, Lijuan Zhang, Zichen Gong, Zhi Beijing Key Laboratory of Trusted Computing Beijing University of Technology Beijing China School of Computer Science Beijing University of Technology Beijing China National Engineering Laboratory for Key Technologies of Information Security Level Protection Beijing China

ISBN: (纸本)9798400718182

Recent deep learning models have advanced object detection capabilities, but their reliance on labeled data limits scalability. Fine-grained zero-shot detection remains challenging due to the need to generalize to unseen classes and distinguish subtle features. To address this, we propose Fine-Grained Zero-Shot Object Detection via Semantic Decoupling (ZSD-SD). Our approach introduces a semantic decoupling module to separate visual features into semantic-related and semantic-unrelated components, facilitating alignment between visual and semantic spaces. A semantic-visual learning module further decouples these features, enhancing visual representations relevant to external semantic knowledge. This semantic-related representation significantly improves zero-shot performance. Experiments on two fine-grained object detection datasets demonstrate that ZSD-SD achieves state-of-the-art results. © 2024 Copyright held by the owner/author(s).

关键词： Zero-shot learning

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A Review on Binary Code Analysis Datasets 18th

A Review on Binary Code Analysis Datasets

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Huang, Zhijian Song, Shuguang Liu, Han Kuang, Hongyu Zhang, Jingjing Hu, Pengfei National Key Laboratory of Science and Technology on Information System Security Systems Engineering Institute Academy of Military Sciences Beijing China School of Computer Science and Technology Shandong University Qingdao China

ISBN: (纸本)9783031714696

Binary code analysis serves as the foundation for research in vulnerability discovery, software protection, and malicious code analysis. However, analyzing binary files is challenging due to the lack of high-level semantic information, leading to heavy dependence on analysts’ expertise and significantly impacting the efficiency of binary code analysis. Recent years has witnessed the blossom of machine learning models for binary analysis, but few researches address the problem of binary code datasets. In this paper, we review all the existing and available datasets, and make classification according to their application. We set up experiments to illustrate how dataset quality could affect the performance of machine learning models in binary function recognition. Based on the experimental evaluation, we present a discussion on the ground truth as well as quality evaluation problems for binary code datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Leveraging semantic relations in code and data to enhance taint analysis of embedded systems 24

Leveraging semantic relations in code and data to enhance ta...

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Proceedings of the 33rd USENIX Conference on security Symposium

作者： Jiaxu Zhao Yuekang Li Yanyan Zou Zhaohui Liang Yang Xiao Yeting Li Bingwei Peng Nanyu Zhong Xinyi Wang Wei Wang Wei Huo Institute of Information Engineering Chinese Academy of Sciences China and School of Cyber Security University of Chinese Academy of Sciences China and Key Laboratory of Network Assessment Technology Chinese Academy of Sciences China and Beijing Key Laboratory of Network Security and Protection Technology China University of New South Wales Australia Institute of Information Engineering Chinese Academy of Sciences China and Key Laboratory of Network Assessment Technology Chinese Academy of Sciences China and Beijing Key Laboratory of Network Security and Protection Technology China

ISBN: (纸本)9781939133441

IoT devices have significantly impacted our daily lives, and detecting vulnerabilities in embedded systems early on is critical for ensuring their security. Among the existing vulnerability detection techniques for embedded systems, static taint analysis has been proven effective in detecting severe vulnerabilities, such as command injection vulnerabilities, which can cause remote code execution. Nevertheless, static taint analysis is faced with the problem of identifying sources comprehensively and *** paper presents LARA, a novel static taint analysis technique to detect vulnerabilities in embedded systems. The design of LARA is inspired by an observation that pertains to semantic relations within and between the code and data of embedded software: user input entries can be categorized as URIs or keys (data), and identifying their handling code (code) and relations can help systematically and comprehensively identify the sources for taint analysis. Transforming the observation into a practical methodology poses challenges. To address these challenges, LARA employs a combination of pattern-based static analysis and large language model(LLM)-aided analysis, aiming to replicate how human experts would utilize the findings during analysis and enhance it. The pattern-based static analysis simulates human experience, while the LLM-aided analysis captures the way human experts perceive code semantics. We implemented LARA and evaluated it on 203 IoT devices from 21 vendors. In general, LARA detects 556 and 602 more known vulnerabilities than SATC and KARONTE while reducing false positives by 57.0% and 54.3%. Meanwhile, with more sources and sinks from LARA, EMTAINT can detect 245 more vulnerabilities. To date, LARA has found 245 0-day vulnerabilities in 57 devices, all of which were confirmed or fixed with 162 CVE IDs assigned.

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Burst image super-resolution based on dual branch fusion and adaptive frame selection

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Displays 2025年 89卷

作者： Duan, Lijuan Zuo, Wendi Gu, Ke Gong, Zhi School of Computer Science Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Trusted Computing Beijing University of Technology Beijing100124 China National Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing University of Technology Beijing100124 China School of Information Science and Technology Beijing University of Technology Beijing100124 China Chongqing Research Institute of Beijing University of Technology China

The modern handheld camera is capable of rapidly capturing multiple images and subsequently merging them into a single image. The extant methodologies typically select the first frame as the reference frame, utilising the information from the remaining frames and the information from the reference frame to calculate the high-resolution image. However, for complex scenes and unstable shooting situations, this fixed frame selection is not the optimum solution. In this direction, we propose an adaptive frame selection method which, by calculating the frame channel weight information, selects the best frame image as the reference frame to perform the subsequent computation. Moreover, to enhance the visual quality of high-resolution images, we propose a dual-branch fusion module in the feature fusion phase for the sequence attributes of the input frame images. This allows the network to concentrate on the temporal global features of the input sequence and the spatial local detail features of the frame images. Subsequently, the feature map is obtained through residual computation utilising the adaptive selected frame image and the image features obtained through fusion. The image is then reconstructed through up-sampling to obtain a high-resolution image. The experimental results on the BurstSR and RealBSR datasets demonstrate that our approach not only outperforms existing techniques in terms of evaluation metrics but also exhibits superior visual effects. © 2025

关键词： Photointerpretation

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Energy-efficient and Privacy-preserving Edge Intelligence for 6G-Empowered Intelligent Transportation Systems

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IEEE Network 2025年

作者： Zhu, Chenlu Fan, Xiaoxuan Deng, Xianjun Xiao, Ziheng Guo, Tingting Liu, Shenghao Sun, Jiaqi Park, Jong Hyuk Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Wuhan Kedao Geographic Information Engineering Co. Wuhan China Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul Korea Republic of

6G-empowered Intelligent Transportation Systems (ITS) generate large amounts of data through millions of devices and sensors at the terminal and network edge. Edge intelligence advances the frontier of data-driven artificial intelligence (AI) to the network edge, which enables AI applications to better utilize edge data. Broader connectivity, higher device density, and stronger data exchange present challenges for reliable device communication and privacy data protection in 6G-empowered ITS. To address these challenges, this paper proposes an energy-efficient and privacy-preserving edge intelligence framework named EP-EI. EP-EI deploys a deep reinforcement learning-based cluster head selection model at the edge to ensure energy-efficient communication for terminal devices. Meanwhile, EP-EI designs a privacy-aware hierarchical computing framework for terminal-edge-cloud to prevent privacy leakage. Furthermore, the application scenarios, challenges, and future directions of EP-EI are discussed. Finally, simulation experiments in a specific intelligent transportation case study demonstrate that EP-EI can provide energy-efficient and privacy-protecting services. © 2025 IEEE.

关键词： Differential privacy

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Gaussian Shading++: Rethinking the Realistic Deployment Challenge of Performance-Lossless Image Watermark for Diffusion Models

arXiv

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arXiv 2025年

作者： Yang, Zijin Zhang, Xin Chen, Kejiang Zeng, Kai Yao, Qiyi Fang, Han Zhang, Weiming Yu, Nenghai University of Science and Technology of China Hefei230026 China Anhui Province Key Laboratory of Digital Security China Department of Information Engineering and Mathematics University of Siena Siena Italy School of Computing National University of Singapore 117417 Singapore

Ethical concerns surrounding copyright protection and inappropriate content generation pose challenges for the practical implementation of diffusion models. One effective solution involves watermarking the generated images. Existing methods primarily focus on ensuring that watermark embedding does not degrade the model performance. However, they often overlook critical challenges in real-world deployment scenarios, such as the complexity of watermark key management, user-defined generation parameters, and the difficulty of verification by arbitrary third parties. To address this issue, we propose Gaussian Shading++, a diffusion model watermarking method tailored for real-world deployment. We propose a double-channel design that leverages pseudorandom error-correcting codes to encode the random seed required for watermark pseudorandomization, achieving performance-lossless watermarking under a fixed watermark key and overcoming key management challenges. Additionally, we model the distortions introduced during generation and inversion as an additive white Gaussian noise channel and employ a novel soft decision decoding strategy during extraction, ensuring strong robustness even when generation parameters vary. To enable third-party verification, we incorporate public key signatures, which provide a certain level of resistance against forgery attacks even when model inversion capabilities are fully disclosed. Extensive experiments demonstrate that Gaussian Shading++ not only maintains performance losslessness but also outperforms existing methods in terms of robustness, making it a more practical solution for real-world deployment. Copyright © 2025, The Authors. All rights reserved.

关键词： Image watermarking

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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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Packet： a privacy-aware access control policy composition method for services composition in cloud environments

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Frontiers of Computer Science 2016年第6期10卷 1142-1157页

作者： Li LIN Jian HU Jianbiao ZHANG College of Computer Science Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Trusted Computing Beijing 100124 China National Engineering Laboratory for Classified Information Security Protection Beijing 100124 China

Combining different independent cloud services must coordinate their access control policies. Otherwise unauthorized access to composite cloud service can occur when there＇s a conflict among different cloud service providers＇ access control policies, and then it will bring serious data security and privacy issues. In this paper, we propose Packet, a novel access control policy composition method that can detect and resolve policy conflicts in cloud service composition, including those conflicts related to privacyaware purposes and conditions. The Packet method is divided into four steps. First, employing a unified description, heterogeneous policies are transformed into a unified attributebased format. Second, to improve the conflict detection ef- ficiency, policy conflicts on the same resource can be eliminated by adopting cosine similarity-based algorithm. Third, exploiting a hierarchical structure approach, policy conflicts related to different resources or privacy-aware purposes and conditions can be detected. Fourth, different conflict resolution techniques are presented based on the corresponding conflict types. We have successfully implemented the Packet method in Openstack platform. Comprehensive experiments have been conducted, which demonstrate the effectiveness of the proposed method by the comparison with the existing XACML-based system at conflict detection and resolution performance.

关键词： cloud service composition access control privacy policy composition unified policy format conflict detection similarity analysis conflict resolution

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