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C-privacy:A social relationship-driven image customization sharing method in cyber-physical networks

Digital Communications and networks 2025年第2期11卷 563-573页

作者： Dapeng Wu Jian Liu Yangliang Wan Zhigang Yang Ruyan Wang Xinqi Lin School of Communications and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Aerospace Tianmu Satellite Technology Company Limited Chongqing401332China School of Cyber Security and Information Law Chongqing University of Posts and TelecommunicationsChongqing400065China College of Computer and Cyber Security Fujian Normal UniversityFujian350117China The Advanced Network and Intelligent Connection Technology Key Laboratory Chongqing400065China Key Laboratory of Ubiquitous Sensing and Networking Chongqing400065China

Cyber-Physical networks(CPN)are comprehensive systems that integrate information and physical domains,and are widely used in various fields such as online social networking,smart grids,and the Internet of Vehicles(IoV).With the increasing popularity of digital photography and Internet technology,more and more users are sharing images on ***,many images are shared without any privacy processing,exposing hidden privacy risks and making sensitive content easily accessible to Artificial Intelligence(AI)*** image sharing methods lack fine-grained image sharing policies and cannot protect user *** address this issue,we propose a social relationship-driven privacy customization protection model for publishers and *** construct a heterogeneous social information network centered on social relationships,introduce a user intimacy evaluation method with time decay,and evaluate privacy levels considering user interest *** protect user privacy while maintaining image appreciation,we design a lightweight face-swapping algorithm based on Generative Adversarial network(GAN)to swap faces that need to be *** proposed method minimizes the loss of image utility while satisfying privacy requirements,as shown by extensive theoretical and simulation analyses.

关键词： Cyber-physical networks Customized privacy Face-swapping Heterogeneous information network Deep fakes

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Semantic-Enhanced Static Vulnerability Detection in Baseband Firmware 24

Semantic-Enhanced Static Vulnerability Detection in Baseband...

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44th ACM/IEEE International Conference on Software engineering, ICSE 2024

作者： Liu, Yiming Zhang, Cen Li, Feng Li, Yeting Zhou, Jianhua Wang, Jian Zhan, Lanlan Liu, Yang Huo, Wei Institute of Information Engineering Cas School of Cyber Security Ucas Beijing China Key Laboratory of Network Assessment Technology Cas China Beijing Key Laboratory of Network Security and Protection Technology China Nanyang Technological University Singapore

ISBN: (纸本)9798400702174

Cellular network is the infrastructure of mobile communication. Baseband firmware, which carries the implementation of cellular network, has critical security impact on its vulnerabilities. To handle the inherent complexity in cellular communication, cellular protocols are usually implemented as message-centric systems, containing the common message processing phase and message specific handling phase. Though the latter takes most of the code (99.67%) and exposed vulnerabilities (74%), it is rather under-studied: existing detectors either cannot sufficiently analyze it or focused on analyzing the former phase. To fill this gap, we proposed a novel semantic-enhanced static vulnerability detector named BVFinder focusing on message specific phase vulnerability detection. Generally, it identifies a vulnerability by locating whether a predefined sensitive memory operation is tainted by any attacker-controllable input. Specifically, to reach high automation and preciseness, it made two key improvements: a semantic-based taint source identification and an enhanced taint propagation. The former employs semantic search techniques to identify registers and memory offsets that carry attacker-controllable inputs. This is achieved by matching the inputs to their corresponding message and data types using textual features and addressing patterns within the assemblies. On the other hand, the latter technology guarantees effective taint propagation by employing additional indirect call resolution algorithms. The evaluation shows that BVFinder outperforms the state-of-the-art detectors by detecting three to four times of amount of vulnerabilities in the dataset. Till now, BVFinder has found four zero-day vulnerabilities, with four CVEs and 12,410 USD bounty assigned. These vulnerabilities can potentially cause remote code execution to phones using Samsung shannon baseband, affecting hundreds of millions of end devices. © 2024 ACM.

关键词： Semantics

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Auto-scalable and fault-tolerant load balancing mechanism for cloud computing based on the proof-of-work election

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Science China(information Sciences) 2022年第1期65卷 131-146页

作者： Xiaoqin FENG Jianfeng MA Shaobin LIU Yinbin MIAO Ximeng LIU School of Cyber Engineering Xidian University State Key Laboratory of Cryptology Shaanxi Key Laboratory of Network and System Security Xidian University College of Mathematics and Computer Science Fuzhou University

Load balancing mechanism in technologies such as cloud computing has provided a huge opportunity for the development of large-scale projects. Although the conventional view is to build mechanisms that adopt a dynamic load balancing strategy, existing strategies cannot automatically scale platform(network) servers(nodes) to adapt for dynamic requests, but only guarantee load balancing for the pre-deployed nodes, thereby increasing resource consumption and decreasing networks' efficiency. We contend that existing load balancing mechanisms are inadequate for deploying dynamic applications. In this regard, we first adopt both load balancing and cloud computing virtualization technologies to modularly design a load balancing mechanism that provides a dynamically auto-scalable solution for large-scale and dynamic computing scenarios. Furthermore, we adopt the proof-of-work consensus, for a novel use during the lifecycle of master nodes in case of system failure caused by a failed master node, to demonstrate a fault-tolerant load balancing mechanism. We theoretically evaluate the security requirement of our mechanism and analyze its performance. Experimental results show that the mechanism supports auto-scalability and has a better performance compared to existing mechanisms such as the ordinary cluster.

关键词： cloud computing load balancing auto-scalable proof-of-work fault-tolerant

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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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SteDM: Efficient Image Steganography with Diffusion Models 1

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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: (数字)9789819615513

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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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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Feature Augmented Meta-Learning on Domain Generalization for Evolving Malware Classification 24th

Feature Augmented Meta-Learning on Domain Generalization fo...

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

作者： Wang, Fangwei Chen, Yinhe Song, Ruixin Li, Qingru Wang, Changguang Key Laboratory of Network and Information Security of Hebei Province College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China College of Computer Science and Technology Guizhou University Guiyang550025 China

ISBN: (纸本)9789819615278

The proliferation of malware has resulted in substantial harm to various sectors and economies. Various deep learning-based malware classification methods have been suggested as a means of mitigating malware threats. These methods typically operate under the assumption of independent and identically distributed training and test data. However, this assumption becomes invalid with the evolving malware family. While domain adaptation models offer a potential solution to this issue, their implementation is hindered by the difficulty of collecting new malware variants. In order to address the previously mentioned problem, we suggest an image-based technique for categorizing malware families utilizing domain generalization. Initially, malware is transformed into gray-scale images that depict byte patterns of the malware. Subsequently, these gray-scale images are fed into a model incorporating convolutional block attention to extract features. Furthermore, data augmentation is implemented at the feature level to broaden the distribution of the source domain and enhance the model’s generalization capabilities. Finally, meta-learning is utilized as a training approach to effectively extract domain-invariant representations. A series of experiments are conducted on the BIG2015 and BenchMFC-G1P1P2. The proposed method demonstrates a higher accuracy rate of 88.66% on the BIG2015 and 80.25% on the BenchMFC-G1P1P2, which is better than the existing methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Domain Generalization Domain Shift Feature Argumentation Malware Classification

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CB-YOLO: A Small Object Detection Algorithm for Industrial Scenarios

CB-YOLO: A Small Object Detection Algorithm for Industrial S...

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

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

ISBN: (纸本)9781665410205

In certain specific industrial scenarios, smoking and cellphone usage are strictly prohibited behaviors. In these scenarios, it is crucial to rapidly and accurately detect smoking and cell phone usage, and promptly issue warnings, to ensure industrial safety. The detection of prohibited behaviors using computer vision has gained attention from researchers with the development of artificial intelligence. However, detecting small objects like cigarettes and cell phones in complex backgrounds and varying angles poses a challenge due to their changing shapes. In this paper, we propose a detection model called CB-YOLO, which utilizes YOLOv7 as the baseline model, for detecting smoking and mobile phone usage behavior. Firstly, we propose a new channel space pyramid network called CSPPF that pools features at different scales and introduces scale focusing on improving the perceptual ability of the network to better handle targets at various scales and locations. Secondly, we propose an enhanced feature pyramid called AWBFPN. This introduces additional learnable weight parameters to improve the model's ability to fuse multi-scale features effectively. Finally, we have also proposed a new loss function called Size-IoU. The experimental results demonstrate that our algorithm outperforms the baseline model. Specifically, it achieves a 3.9% improvement in Precision, a 2.1% improvement in Recall, a 6.4% improvement in mAP@0.5, and a 2% improvement in mAP@0.5:0.95 on the Phone_check dataset. Similarly, on the Smoking dataset, our algorithm achieves a 3.3% improvement in Precision, a 1.9% improvement in Recall, a 1.7% improvement in mAP@0.5, and a 0.3% improvement in mAP@0.5:0.95. © 2024 IEEE.

关键词： Object detection

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BTP-CAResNet: An Encrypted Traffic Classification Method Based on Byte Transfer Probability and Coordinate Attention Mechanism

BTP-CAResNet: An Encrypted Traffic Classification Method Bas...

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

作者： Li, Junhao Shi, Huiling Zhang, Wei Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781665410205

With the extensive application of network traffic encryption technology, the accurate and efficient classification of encrypted traffic has become a critical need for network management. Deep learning has become the predominant method for traffic classification, primarily involving the transformation of network traffic into grayscale images and their subsequent classification using Convolutional Neural networks (CNNs). However, traditional grayscale image generation methods are plagued with issues of redundant and lost information, and conventional channel attention mechanisms are still insufficient in capturing key traffic features, collectively hindering the enhancement of classification performance. To tackle these issues, this paper introduces a classification method based on Byte Transfer Probability and Coordinate Attention Mechanism in Residual network (BTP-CAResNet). This method, on the foundation of the classic ResNet architecture, incorporates a new grayscale image generation method that utilizes Byte Transfer Probability, effectively overcoming the deficiencies of traditional approaches. Additionally, this paper integrates a Coordinate Attention Mechanism into the ResNet model, which effectively overcomes the limitations of traditional channel attention mechanisms and further improves the performance of traffic classification. Experimental validation on the ISCX VPN-nonVPN dataset demonstrates that, compared to previous CNN-based methods, the method proposed in this paper exhibits superior performance in key metrics such as accuracy, precision, recall, and F1 score. It provides a new perspective for traffic classification based on convolutional neural networks. © 2024 IEEE.

关键词： Convolutional neural networks

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RFSD-YOLO: An Enhanced X-Ray Object Detection Model for Prohibited Items

RFSD-YOLO: An Enhanced X-Ray Object Detection Model for Proh...

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

作者： Kong, Xiaotong Li, Aimin Li, Wenqiang Li, Zhiyao Zhang, Yuechen Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

X-ray image detection is essential for ensuring public safety, but traditional methods rely heavily on human analysis and are relatively inefficient. To address this issue, this paper proposes a new object detection model called RFSD-YOLO, which is based on the YOLOv8 model and is specifically designed for detecting prohibited items in X-ray images. The model adopts the RFCAConv structure instead of the conventional convolutional operation. This allows for independent parameterisation among the convolutional kernels, enhancing the model's ability to capture and express potential prohibited item features. The neck section of the model includes the GSConv and VoVgscspdesigns, which aim to balance complexity and parameter size while maintaining detection performance and reducing computational burden. Additionally, we have introduced a dynamic head structure, DyHead, which replaces the traditional detection head design and improves detection accuracy without adding computational cost. The experimental results demonstrate that our enhanced model surpasses the current state-of-the-art object detection models in detecting prohibited items. Additionally, we introduce a simplified version of the RFSD-YOLOnano model to cater to resource-constrained environments. This streamlined model improves AP, AR, and mAP by 4.4%, 14.6%, and 3.4%, respectively, compared to YOLOv8n. This series of innovations not only validates the superiority of our model, but also provides new solutions in the field of automated detection of prohibited items for X-ray security screening. © 2024 IEEE.

关键词： Object recognition

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