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Multi-class Bitcoin mixing service identification based on graph classification

Digital Communications and networks 2024年第6期10卷 1881-1893页

作者： Xiaoyan Hu Meiqun Gui Guang Cheng Ruidong Li Hua Wu School of Cyber Science&Engineering Southeast UniversityNanjing 211189China Purple Mountain Laboratories for Network and Communication Security Nanjing 211111China Jiangsu Provincial Engineering Research Center of Security for Ubiquitous Network Nanjing 211189China Key Laboratory of Computer Network and Information Integration Southeast UniversityMinistry of EducationChina Research Base of International Cyberspace Governance Southeast UniversityNanjing 211189China Institute of Science and Engineering Kanazawa UniversityKakumaKanazawa920-1192Japan

Due to its anonymity and decentralization,Bitcoin has long been a haven for various illegal *** generally legalize illicit funds by Bitcoin mixing ***,it is critical to investigate the mixing services in cryptocurrency anti-money *** studies treat different mixing services as a class of suspicious Bitcoin ***,they are limited by relying on expert experience or needing to deal with large-scale *** far,multi-class mixing service identification has not been explored *** is challenging since mixing services share a similar procedure,presenting no sharp ***,mixing service identification facilitates the healthy development of Bitcoin,supports financial forensics for cryptocurrency regulation and legislation,and provides technical means for fine-grained blockchain *** paper aims to achieve multi-class Bitcoin Mixing Service Identification with a Graph Classification(BMSI-GC)***,BMSI-GC constructs 2-hop ego networks(2-egonets)of mixing services based on their historical ***,it applies graph2vec,a graph classification model mainly used to calculate the similarity between graphs,to automatically extract address features from the constructed ***,it trains a multilayer perceptron classifier to perform classification based on the extracted ***-GC is flexible without handling the full-size network and handcrafting address ***,the differences in transaction patterns of mixing services reflected in the 2-egonets provide adequate information for *** experimental study demonstrates that BMSI-GC performs excellently in multi-class Bitcoin mixing service identification,achieving an average identification F1-score of 95.08%.

关键词： Bitcoin Anti-money laundering Mixing service Ego network Graph classification

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SteDM: Efficient Image Steganography with Diffusion Models 24th

SteDM: Efficient Image Steganography with Diffusion Models

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Wang, Changguang Shi, Haoyi Li, Qingru Zhao, Dongmei Wang, Fangwei Key Laboratory of Network and Information Security of Hebei Province Hebei Normal University Shijiazhuang050024 China College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China

ISBN: (纸本)9789819615506

Existing deep learning-based image steganography schemes mostly neglect the latent space of images. These schemes merely adopt simple concatenation of image feature vectors, resulting in a low utilization rate of features, low steganographic image quality, and poor image robustness. This paper introduces the latent diffusion model into an image steganography scheme, namely SteDM. The SteDM firstly uses an encoder to transform the cover image and the secret image into the latent space, then employing a cross-attention mechanism to fuse them during the inverse diffusion process. Then we use a decoder to obtain a steganographic image containing secret image features. During the extraction process, the latent space-based diffusion model is similarly employed. Training loss is defined as a joint optimization of the autoencoder and diffusion model during the training process. Experimental results demonstrate that the SteDM outperforms existing steganography schemes in some aspects such as visual effects, security, and robustness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Steganography

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Reverse-Nearest-Neighbor-Based Clustering by Fast Search and Find of Density Peaks

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Chinese Journal of Electronics 2023年第6期32卷 1341-1354页

作者： ZHANG Chunhao XIE Bin ZHANG Yiran College of Computer and Cyber Security Hebei Normal University Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security Hebei Normal University Hebei Provincial Key Laboratory of Network and Information Security Hebei Normal University

Clustering by fast search and find of density peaks(CFSFDP) has the advantages of a novel idea, easy implementation, and efficient clustering. It has been widely recognized in various fields since it was proposed in Science in 2014. The CFSFDP algorithm also has certain limitations, such as non-unified sample density metrics defined by cutoff distance, the domino effect for the assignment of remaining samples triggered by unstable assignment strategy, and the phenomenon of picking wrong density peaks as cluster centers. We propose reverse-nearest-neighbor-based clustering by fast search and find of density peaks(RNN-CFSFDP) to avoid these shortcomings. We redesign and unify the sample density metric by introducing reverse nearest neighbor. The newly defined local density metric and the K-nearest neighbors of each sample are combined to make the assignment process more robust and alleviate the domino effect. A cluster fusion algorithm is proposed, which further alleviates the domino effect and effectively avoids the phenomenon of picking wrong density peaks as cluster centers. Experimental results on publicly available synthetic data sets and real-world data sets show that in most cases, the proposed algorithm is superior to or at least equivalent to the comparative methods in clustering performance. The proposed algorithm works better on manifold data sets and uneven density data sets.

关键词： Measurement Manifolds Shape Clustering algorithms Synthetic data

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An Adaptive Residual Coordinate Attention-Based network for Hat and Mask Wearing Detection in Kitchen Environments

An Adaptive Residual Coordinate Attention-Based Network for ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhao, Ying Wu, Xiaoming Liu, Xiangzhi Chen, Hao Qi, Bei Dong, Huomin Key Laboratory of Computing Power Network and Information Security Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

In order to ensure food safety, it is required for personnel to wear hats and masks during food handling processes. To accurately detect the wearing status of kitchen staff, the ARP-YOLO model is proposed. Firstly, images are obtained from multiple kitchens and angles to construct a dataset reflecting the wearing status of hats and masks. To simulate more complex kitchen environments, Gaussian noise is added to the data and lighting conditions are adjusted for data augmentation. Lighting conditions in the kitchen can affect detection, causing the same target to exhibit different shapes and features under different lighting conditions, leading to missed detections. To address the above issues, we propose ARCA (Adaptive-Residual-Coordinate-Attention), which uses residual connections to strengthen attention to important features while preserving original features, and employs adaptive convolution reduction to reduce module parameters. To improve target localization accuracy, P2 detection layers are added in the Neck to obtain more accurate target position information. The ARP-YOLO model demonstrates significant improvements over the baseline model, with a 13.6% increase in Recall, allowing for more effective target capture and reduced missed detection risk. Additionally, mAP@0.5 has increased by 10%, enhancing target localization accuracy. The F1-Score has also increased by 7.4%, better balancing the relationship between Precision and Recall. To validate the effectiveness of the model, comparative experiments with other models are conducted, showing that ARP-YOLO model's Recall and Average Precision(AP) are higher than those of other models. © 2024 IEEE.

关键词： Lighting

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Regularization Mixup Adversarial Training: A Defense Strategy for Membership Privacy with Model Availability Assurance 2

Regularization Mixup Adversarial Training: A Defense Strateg...

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2nd International Conference on Big Data and Privacy Computing, BDPC 2024

作者： Ding, Zehua Tian, Youliang Wang, Guorong Xiong, Jinbo College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang China College of Computer and Cyber Security Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou China

ISBN: (纸本)9798350307597

Neural network models face two highly destructive threats in real-world applications: membership inference attacks (MIAs) and adversarial attacks (AAs). One compromises the model's confidentiality, leading to membership privacy breaches, while the other disrupts the model's availability, rendering it dysfunctional. Recent work has shown that adversarial examples can pose dual threats to neural network models, both MIAs and AAs, so that these two different types of attacks have a certain degree of intersection. However, existing defense methods typically focus on defending against one of the two attacks and cannot simultaneously address both. To address the above issues, we propose a defense method called regularization mixup adversarial training (RMAT), aiming to protect model membership privacy while ensuring model availability. The core idea of RMAT is to use a continuous augmentation strategy during training to generate mixup adversarial examples, weakening attackers' understanding of membership data, and updating the model with a new membership-weighted loss strategy to improve its generalization ability for handling unknown samples. This approach can further protect the model's membership privacy and ensure model availability. Experimental results demonstrate that compared to single defense strategies, RMAT can simultaneously protect the model's membership privacy and availability. © 2024 IEEE.

关键词： Adversarial machine learning

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A Hypothesis Testing-based Cyber Deception Framework under Sequential Reconnaissance 59

A Hypothesis Testing-based Cyber Deception Framework under S...

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59th Annual Conference on Information Sciences and Systems, CISS 2025

作者： Kumar, Abhinav Brahma, Swastik Kamhoua, Charles A. University of Cincinnati Department of Computer Science OH United States Devcom Army Research Laboratory Network Security Branch MD United States

ISBN: (纸本)9798331513269

In this paper, we present a novel sequential hypothesis testing framework to model an attacker's process of gathering and processing information sequentially during the reconnaissance phase. The attacker's optimal sequential decision rule, that enables it to promptly terminate its sequential reconnaissance process when its gathered information yields a desired decision-making quality, has been characterized. Further, employing the developed framework, we characterize optimal strategies that a defender can employ to feed the attacker with falsified information such that the expected time taken by the attacker for its malicious decision-making is maximally prolonged. Criterion that such falsification strategies must satisfy to blind the attacker and make it take indefinitely long to decide has been presented. Numerical results have been presented to provide important insights into the developed strategies. © 2025 IEEE.

关键词： Phishing

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Textual Data De-Privatization Scheme Based on Generative Adversarial networks 24th

Textual Data De-Privatization Scheme Based on Generative Adv...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Du, Yanning Xu, Jinnan Zhang, Yaling Wang, Yichuan Wang, Zhoukai School of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

ISBN: (纸本)9789819615506

In many fields, such as healthcare, finance, and scientific research, data sharing and collaboration are critical to achieving better outcomes. However, the sharing of personal data often involves privacy risks, so privacy-preserving techniques are needed to ensure data security and privacy. The superior performance and flexibility of generative models in data representation have led to significant progress in the development of data privacy. This paper proposes a textual data de-privatization scheme based on generative adversarial networks that well combines generative adversarial networks with privacy protection. The generated de-privacy data retains the statistical properties of the original data and effectively removes sensitive information. Moreover, feature sorting and reshaping modules are introduced to enable the generator to better capture the relationships between features, thus improving the quality of synthetic data. In this paper, the utility of synthetic data is evaluated from three aspects, including similarity assessment, privacy assessment, and model utility assessment. Experimental results show that this method achieves a good trade-off in terms of textual data privacy protection and data quality maintenance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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A Sharding Scheme Based on Graph Partitioning Algorithm for Public Blockchain

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computer Modeling in Engineering & Sciences 2024年第6期139卷 3311-3327页

作者： Shujiang Xu Ziye Wang Lianhai Wang Miodrag J.Mihaljevi′c Shuhui Zhang Wei Shao Qizheng Wang Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science CenterQilu University of Technology(Shandong Academy of Sciences)Jinan250014China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan250014China Mathematical Institute The Serbian Academy of Sciences and ArtsBelgrade11000Serbia

Blockchain technology,with its attributes of decentralization,immutability,and traceability,has emerged as a powerful catalyst for enhancing traditional industries in terms of optimizing business ***,transaction performance and scalability has become the main challenges hindering the widespread adoption of *** to its inability to meet the demands of high-frequency trading,blockchain cannot be adopted in many *** improve the transaction capacity,researchers have proposed some on-chain scaling technologies,including lightning networks,directed acyclic graph technology,state channels,and shardingmechanisms,inwhich sharding emerges as a potential scaling ***,excessive cross-shard transactions and uneven shard workloads prevent the sharding mechanism from achieving the expected *** paper proposes a graphbased sharding scheme for public blockchain to efficiently balance the transaction *** crossshard transactions and evening-out workloads among shards,the scheme reduces transaction confirmation latency and enhances the transaction capacity of the ***,the scheme can achieve a high-frequency transaction as well as a better blockchain *** results show that the scheme effectively reduces the cross-shard transaction ratio to a range of 35%-56%and significantly decreases the transaction confirmation latency to 6 s in a blockchain with no more than 25 shards.

关键词： Blockchain sharding graph partitioning algorithm

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Neural network-Based State Estimation for Nonlinear Systems With Denial-of-Service Attack Under Try-Once-Discard Protocol

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IEEE/CAA Journal of Automatica Sinica 2024年第10期11卷 2182-2184页

作者： Xueli Wang Shangwei Zhao Ming Yang Xin Wang Xiaoming Wu the Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science CenterQilu University of Technology(Shandong Academy of Sciences)Jinan 250014 Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250014China Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China

Dear Editor,This letter deals with state estimation issues of discrete-time nonlinear systems subject to denial-of-service(DoS)attacks under the try-once-discard(TOD)*** specifically,to reduce the communication burden,a TOD protocol with novel update rules on protocol weights is designed for scheduling measurement *** addition,unknown nonlinear functions vulnerable to DoS attacks are considered due to the openness and vulnerability of the network.

关键词： Service DoS service

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A bitcoin service community classification method based on Random Forest and improved KNN algorithm

IET Blockchain

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IET Blockchain 2024年第3期4卷 276-286页

作者： Gao, Muyun Lin, Shenwen Tian, Xin He, Xi He, Ketai Chen, Shifeng School of Mechanical Engineering University of Science and Technology Beijing Beijing China Internet Financial Security Technology Key Laboratory National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing China

There are service communities with different functions in the Bitcoin transactions system. Identifying community categories helps to further understand the Bitcoin transactions system and facilitates targeted regulation of anonymized Bitcoin transactions. To this end, a Bitcoin service community classification method based on Random Forest and improved K-Nearest Neighbor (KNN) algorithm is proposed. First, the transaction characteristics of different types of communities are analyzed and summarized, and the corresponding transaction features are extracted from the address and entity levels;then multiple classification algorithms are compared, the optimal model to filter the effective features is selected, and the feature vector of entity addresses is constructed. Finally, a classification model is constructed based on Random Forest and improved KNN algorithm to classify the entities. By constructing different classification models for experimental comparison, the accuracy and stability advantages of the proposed method for classification in service community classification research are verified. © 2024 The Authors. IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Classification (of information)

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