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2nd International Conference on Data security and Privacy Protection, DSPP 2024

作者： An, Jiayi Tian, Haibo Guangdong Province Key Laboratory of Information Security Technology School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China

ISBN: (纸本)9789819785391

Constructing covert channels on blockchains has recently become a significant research focus. A major challenge lies in embedding data into blockchain transactions, while maintaining strong concealment. This study focuses on utilizing blockchain transaction values as a carrier, exploring methods to ensure that the distribution of values with embedded data mimics that of standard transactions. Initially, we gathered authentic blockchain transaction data, conducted a frequency analysis on the transaction values, and filtered out abnormal values to establish a baseline normal value dataset. Subsequently, we employed a Restricted Boltzmann Machine (RBM) to generate a simulated value dataset that mirrors the distribution of the normal dataset, facilitating the embedding of data within the simulated value dataset. By fine-tuning the number of bits embedded in the values, we achieved a value dataset with embedded data that closely resembles the normal distribution. We have calculated the Kullback-Leibler Divergence and the Kolmogorov-Smirnov Test between the value dataset with embedded data and the normal value dataset under various embedding scenarios, revealing that the changes to the original dataset are minimal. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Normal distribution

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A Reversible Data Hiding in Encryption Domain for JPEG Image Based on Controllable Ciphertext Range of Paillier Homomorphic Encryption Algorithm 21st

A Reversible Data Hiding in Encryption Domain for JPEG Ima...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Ma, Bin Zhao, Chunxin Ma, Ruihe Xian, Yongjin Wang, Chunpeng Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China School of Economics Jilin University Changchun130015 China

ISBN: (纸本)9789819601219

With the rapid advancement of cloud computing, significant security concerns regarding data storage have been continuously raised. When the encrypted images are stored in the cloud, reversible data hiding in encrypted domain is used to embed additional data to manage the storage resources in the cloud. Most of the current works are applied to uncompressed images rather than JPEG images. Therefore, this paper proposes a reversible data hiding algorithm for JPEG images based on an enhanced Paillier homomorphic encryption algorithm. In the proposed scheme, the AC coefficients are altered by using a ciphertext distribution constrained paillier homomorphic encryption algorithm, so as to reduce the size of the data-embedded encrypted image. Whereas, the DC coefficients are encrypted by using the one-dimensional tangent chaos encryption algorithm, thereby ensuring high security of the encrypted image. Moreover, according to the characteristics of the homomorphic encryption algorithm, the confidential data can be recovered from both the encryption domain and from the plain domain. Experimental results demonstrate that the ciphertext distribution-constrained Paillier homomorphic encryption algorithm obtains higher data embedding capacity and superior security in comparison with other advanced algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Encryption algorithms

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Online Queue-Aware Service Migration and Resource Allocation in Mobile Edge Computing

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8063-8078页

作者： Du, An Jia, Jie Chen, Jian Wang, Xingwei Huang, Ming Northeastern University School of Computer Science and Engineering Engineering Research Center of Security Technology of Complex Network System Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Northeastern University School of Computer Science and Engineering Shenyang110819 China

Mobile edge computing (MEC) integrated with network Functions Virtualization (NFV) helps run a wide range of services implemented by Virtual network Functions (VNFs) deployed at MEC networks. This emerging paradigm offers flexible edge resource management for Internet Service Providers (ISPs) and improves service satisfaction of diverse applications. However, a critical challenge lies in processing extremely unpredictable and bursty traffic flow, especially in event-trigger sensing tasks. In this paper, we study queue-aware service migration and resource allocation in an MEC network, where different devices with different arrival patterns request different network services. To this end, we first formulate a novel long-term operation cost minimization problem subject to the stability of multiple queues by adopting a simultaneous migration scheme and determining resource allocation dynamically. To address the problem efficiently, we propose an online control framework to make decisions without any prior system information. We first adopt the Lyapunov optimization technique to decompose the long-term optimization problem into multi-per-slot subproblems. Then, we adopt a decoupled optimization algorithm based on the coordinate descent method and augmented Lagrangian method to solve the offline mixed integer non-linear programming (MINLP) subproblems. Extensive results reveal that the proposed online optimization framework can efficiently balance queue stability and cost consumption. © 1967-2012 IEEE.

关键词： Mobile edge computing

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A study of watermarking techniques for data publishing

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Multimedia Tools and Applications 2025年 1-30页

作者： Yuan, Shuguang Yu, Jing Yang, Ke Wang, Yuyang Li, Zhaochen Qiu, Jiabao Yang, Tengfei Chen, Chi Institute of Information Engineering Chinese Academy of Science Beijing China State Key Laboratory of Cyberspace Security Defense Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing China

In today’s digital era, data sharing is becoming an emerging trend. However, copying, manipulating, and distributing multimedia data over an open channel is easy. Watermarking is one of the most promising methods for copyright protection, tamper detection, and traitor tracing tasks. In the past decades, many diversified watermarking techniques have been created and are still developing, which entails various requirements and challenges. In this paper, we survey recent watermarking techniques on data publishing scenarios. The main contributions of this paper include: 1) We explain the classification methods and background knowledge of watermarking techniques, such as types of function, watermarking process, and basic concepts;2) We primarily survey watermarking techniques for data publishing on different carriers, i.e., relational database, text, print and social media;3) We give representation figures and detailed analysis tables for the essential researches;4) Finally, it provides a discussion of future work according to analysis and observation of existing works. This survey allows researchers, users, and engineers to use efficient watermarking tools to secure published data. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Watermarking

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Quasi-Linearly Homomorphic Signature for Data Integrity Auditing in Cloud Storage 20th

Quasi-Linearly Homomorphic Signature for Data Integrity Aud...

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

作者： Zhang, Futai Huang, Yichi Yang, Wenjie Tian, Jinmei Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fujian Normal University Fuzhou350117 China

ISBN: (纸本)9789819647330

As the popularity of cloud storage increases, data security issues have attracted significant attention from both academia and business sectors. Among these issues, data integrity auditing is of particular importance. To date, numerous data integrity auditing protocols for cloud storage have been available in the literature. Most of them are based on public key cryptography. In this paper, we examine the key technique involved in public key data integrity auditing in cloud storage. The concept and the security model of quasi-linearly homomorphic signature(QLHS) scheme are introduced. Its relationship with secure public key data integrity auditing in cloud storage is demonstrated. An efficient instantiation of the quasi-linearly homomorphic signature scheme is presented. The performance of a provable data possession(PDP) protocol using our QLHS scheme is analyzed. The comparison demonstrates that our approach is more efficient in terms of computation, communication, and storage space for cloud users. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Public key cryptography

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Unsigned Road Incidents Detection Using Improved RESNET From Driver-View Images

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1203-1216页

作者： Li, Changping Wang, Bingshu Zheng, Jiangbin Zhang, Yongjun Chen, C.L. Philip Northwestern Polytechnical University School of Software Xi’an710129 China Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

Frequent road incidents cause significant physical harm and economic losses globally. The key to ensuring road safety lies in accurately perceiving surrounding road incidents. However, the highly dynamic nature of traffic introduces significant challenges, particularly in detecting sudden and temporary incidents. In this article, we propose a novel detection framework, multihead attention ResNet with dynamic bottleneck (DB_RESNET_MHA), to identify physical unsigned road incidents. Our approach introduces three key innovations. First, we develop a tailored data augmentation strategy to create images that closely mimic the complex variations found in real-world road environments. Second, we enhance model expressiveness by employing attention mechanisms to nonlinearly integrate convolutional kernels within the residual network. Furthermore, we refine the prediction head by applying spatially distinct attention weights, enabling the model to capture intricate correlations between different features more effectively. To demonstrate the effectiveness of our method, we create a dataset for unsigned road incidents (UnsignRI), comprising a total of 16 323 images that capture 12 distinct types of incidents. It stands out as the most comprehensive dataset in the field, encompassing a wide range of geographical features and incident categories. Experimental results show that DB_RESNET_MHA achieves an average accuracy of 96.2% and a f1-score of 0.955 across various categories of unsigned incidents, surpassing other models. © 2020 IEEE.

关键词： Highway accidents

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A circuit area optimization of MK-3 S-box

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Cybersecurity 2025年第1期8卷 61-71页

作者： Yanjun Li Weiguo Zhang Yiping Lin Jian Zou Jian Liu Information Industry Information Security Evaluation Center The 15th Research Institute of China Electronic Technology Group CorporationBeijing 100083China Henan Key Laboratory of Network Cryptography Technology Zhengzhou 450001China Beijing Electronic Science and Technology Institute No.7 Fufeng RoadFengtai DistricBeijing 100083China College of Computer and Data Science Fuzhou UniversityFuzhou 350108China

In MILCOM 2015,Kelly et *** the authentication encryption algorithm MK-3,which applied the 16-bit *** paper aims to implement the 16-bit S-box with less circuit ***,we classifed the irreducible polynomials over F_(2n)into three *** we compared the logic gates required for multiplication over the fnite feld constructed by the three types of irreducible *** to the comparison result,we constructed the composite felds, F_((2^(4))^(2))and F_((2^(8))^(2)).Based on the isomorphism of fnite felds,the operations over F_(2)^(16)can be conducted over F_((2^(8))^(2)).Similarly,elements over F28 can be mapped to the corresponding elements over F_((2^(4)))^(2).Next,the SAT solver was used to optimize the operations over smaller feld F_(2)^(4).At last,the architecture of the optimized MK-3 S-box was worked *** with the implementation proposed by the original designer,the circuit area of the MK-3 S-box in this paper is reduced by at least 55.9%.

关键词： irreducible operations optimization

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Enhancing Parameter-Efficient Transformers with Contrastive Syntax and Regularized Dropout for Neural Machine Translation 21st

Enhancing Parameter-Efficient Transformers with Contrastive...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Fan, Yunlong Yang, Zhiheng Li, Baixuan Gao, Zhiqiang School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China

ISBN: (纸本)9789819601219

Transformers have significantly improved Neural Machine Translation (NMT) models, accompanied by the inherent space complexity of O(n2). While recent approaches aim to be parameter-efficient, they often exhibit limited generalization capabilities with fewer parameters and decreased performances in longer sentences. To this end, we propose two methods, Syntax-enhanced Contrastive Learning (Syn-CL) and JS divergence-based Regularized Dropout (JSR-Drop) for the cross-layer parameter-sharing Transformer (baseline) in NMT. In Syn-CL, we add the corresponding target-to-source instances for the same batch in an extra bidirectional training phase, and minimize the distances of syntax-enhanced representation between the bilingual sentence-pair while maximizing the ones among others. In JSR-Drop, we refine the regularized dropout strategy with JS divergence, and improve generalization capabilities by reducing Transformers’ inconsistencies between training and inference introduced by dropout. Extensive experiments on six NMT tasks, IWSLT2014 (German↔English) and IWSLT2017 (English↔French and English↔Romanian), show an average BLEU score of 37.55, surpassing the vanilla Transformer and the baseline up to 1.75 BLEU, and outperforming models with 1.5×, 2.0× parameters in translating long sentences. Our code is available at https://***/Anon1214/EnPeNMT. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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DSDIR: A Two-Stage Method for Addressing Noisy Long-Tailed Problems in Malicious Traffic Detection

DSDIR: A Two-Stage Method for Addressing Noisy Long-Tailed P...

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

作者： Li, Guoliang Zhang, Ruiqi Sun, Zhe Xing, Lingkai Zhang, Yu College of Cyber Science Nankai University Tianjin China College of Computer Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China Key Laboratory of Data and Intelligent System Security Ministry of Education Tianjin China

ISBN: (纸本)9798350368741

In recent years, deep learning based malicious traffic detection (MTD) systems have demonstrated remarkable success. However, their effectiveness tend to decrease because most malicious traffic datasets are suffered from noisy-labeled and long-tailed problems. While numerous approaches have been developed to address these two problems individually, they become inefficient when confronting the combined challenge of noisy long-tailed data, as they typically tackle only a single adverse factor at a time. This paper proposes a two-stage method called Distribution-aware sample Selection and Dynamic Instance-based Relabeling (DSDIR), which simultaneously addresses the impacts of noisy-labeled and long-tailed problems. In the first stage, a noise-independent clean sample selection method is designed to obtain a clean dataset, which converts negative effects of the long-tailed problem into positive ones. In the second stage, dynamic instance-based relabeling is designed to train a model and improve the dataset's quality simultaneously. Eventually, DSDIR not only produces a balanced and noise-tolerant model but also obtains a clean dataset. Experimental results demonstrate that in the high noise condition of 60% and 80%, the accuracy rate of DSDIR is 5% higher than the state-of-the-art methods. Our code is available at https://***/nku-ligl/DSDIR. © 2025 IEEE.

关键词： deep learning distribution estimation dynamic relabeling malicious traffic detection noisy long-tailed problem

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FirmPorter: Porting RTOSes at the Binary Level for Firmware Re-hosting 26th

FirmPorter: Porting RTOSes at the Binary Level for Firmwa...

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26th International Conference on information and Communications security, ICICS 2024

作者： Xin, Mingfeng Wen, Hui Deng, Liting Li, Hong Li, Qiang Sun, Limin Beijing Key Laboratory of IOT Information Security Technology Institute of Information Engineering CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Computer and Information Technology Beijing Jiaotong University Beijing China

ISBN: (纸本)9789819788002

The rapid growth of the Industrial Internet of Things (IIoT) has brought real-time operating system (RTOS) into focus as major targets for both security analysts and malicious adversaries. Due to the non-standard hardware and diverse software, embedded RTOS devices present unique challenges to security analysts for the accurate analysis of firmware binaries. The diversity in hardware components and tight coupling between firmware and hardware makes it hard to perform dynamic analysis, which must have the ability to execute firmware code in virtualized environments. However, emulating the large expanse of hardware peripherals makes analysts have to frequently modify the emulator for executing various firmware code in different virtualized environments, which greatly limits the ability of analysis. In this work, we explore the problem of firmware re-hosting related to the RTOS. A device driver is developed by developers so that RTOS can be run on their platform. By providing alternative implementations for device drivers, we can make minimal modifications to the firmware that is to be migrated from its original hardware environment into a virtualized one. We show that an approach is capable of offering the ability to emulate various RTOS firmware in an automated manner without modifying existing emulators. Our approach, called static binary-level driver porting, first locates device driver initialization function and identify driver types in the target firmware, then adapts pre-built drivers to the existing emulator hardware. Finally, it replaces the drivers in the firmware with ours by utilizing binary rewriting technique. We demonstrate the practicality of the proposed method on multiple hardware platforms and firmware samples for security analysis. The results show that the approach is flexible enough to emulate firmware for vulnerability assessment and exploit development. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Firmware

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