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International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC

作者： Minkang Zhang Ding Chen Yongxin Huang School of Cyberspace Security Chengdu University of Information Technology(CUIT) Chengdu China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu China

Pedestrian reidentification is a popular research topic in the field of computer vision in recent years, and is a technique that uses computer vision techniques to determine whether a specific pedestrian is present in an image or video. After research and experiment, we found that YOLOv3-based pedestrian reidentification in practice has the problem of low accuracy rate of recognizing pedestrian pictures taken at night and cannot recognize multiple pedestrians at one time. In this paper, we improve the above problems by introducing a picture enhancement module to improve the brightness and defogging of night pictures before recognition, and improve the practice of averaging the distance values of multiple results for the same pedestrian to enable multiple targets pedestrian recognition. The experimental results demonstrate that the average accuracy rate of recognizing pedestrian pictures taken at night has improved from 6.85% to 80%, while the average accuracy rate of multiple targets pedestrian recognition has reached 85.9%, which is competent for multiple targets pedestrian recognition tasks at night.

关键词： Computer vision Image recognition Image resolution Target recognition Brightness Lighting Knowledge discovery

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Quantum K-Means Model based on Optimization Framework

Quantum K-Means Model based on Optimization Framework

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Image Processing, Electronics and Computers (IPEC), Asia-Pacific Conference on

作者： Zexiang Shao Shibin Zhang Sachin Kumar School of Cyberspace Security Chengdu University of Information Technology Chengdu China Sichuan Key Laboratory of Advanced Cryptography and System Security Chengdu China Department of Computer Science and Engineering Ajay Kumar Garg Engineering College India

The Quantum K-means clustering algorithm offers the advantage of quantum parallel computing, but suffers from issues related to cluster center initialization and sensitivity to noisy data due to its similarity with the K-means clustering algorithm. To address these challenges, we propose a quantum K-means model based on an optimization framework. Specifi-cally, we use a quantum genetic algorithm with pin optimization to adjust the fitness function for the Quantum K-means clustering algorithm. The optimally adapted quantum genetic algorithm reduces the sensitivity of cluster center initialization and improves the noise robustness of the Quantum K-means clus-tering algorithm using a noise reduction self-encoder. As a result, the proposed model achieves better clustering performance even in the presence of noise. Experimental results demonstrate that our proposed quantum K-means model has fewer clustering iterations, lower probability of falling into local optima, and improved noise robustness after optimizing the clustering cen-ters. The model's effectiveness in clustering is also demon-strated experimentally.

关键词：

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Improved YOLOv5s algorithm for small item detection of wheelhouse

Improved YOLOv5s algorithm for small item detection of wheel...

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International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC

作者： Jin Hu Wang Juan Wang Zuli Long Dan School of Cyberspace Security Chengdu University of Information Technology(CUIT) Chengdu China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu China

With the development of deep learning, object detection has achieved rapid development in recent years, and is widely used in real-life scenarios such as face detection and automatic driving. In the field of ship navigation safety, it is necessary to identify whether some specific items appear in the wheelhouse to help determine whether there is a threat to drive safety. These items are usually small in size and require higher detection efficiency. To address this problem, this paper proposes a ship-specific item detection method that improves the YOLOv5s algorithm. By introducing the convolution attention mechanism module CBAM, the feature extraction ability of the network, the detection capability of small targets, and the detection accuracy are improved. The experimental results show that after the introduction of the attention mechanism, the precision rate of YOLOv5s on ship-specific items is 85.6%, the recall rate is 85.2%, and the average accuracy is 90.2%, which can complete the detection task of specific items of wheelhouse small targets

关键词： Convolution Navigation Object detection Feature extraction Knowledge discovery Safety Face detection

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An Image Semantic Communication system Based on GAN and Relative Position-Encoding

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IEEE Communications Letters 2025年

作者： Zhang, Ziqi Tan, Weijie Li, Chunguo Guizhou University College of Big Data and Information Engineering Guiyang550025 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610025 China Ministry of Industry and Information Technology Laboratory of Cryptography Application Technology Innovation and Testing Verification Beijing100015 China Southeast University School of Information Science and Engineering Nanjing210096 China

In image semantic communication, the complex wireless channel environment leads to the loss of image details and performance degradation during transmission. To address this issue, we propose an image semantic communication system based on Generative Adversarial Networks (GAN) and Relative Position-Encoding (RPE), named GAN-RpeSC. Specifically, in the system design, we introduce the GAN network at the output of the semantic communication framework to enhance the image restoration. We integrate the RPE module into the Visual Transformer (ViT) module, adding the RPE module to its Multihead Self Attention (MHSA), so that the model can better capture the spatial location information of the image. Experimental results show that the proposed GAN-RpeSC has significant advantages in improving image transmission quality, with a 0.3 dB improvement in peak signal-to-noise ratio (PSNR) compared to the ViT module alone. © 1997-2012 IEEE.

关键词： Encoding (symbols)

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A Blockchain Based system for Multi-level Log Storage 7

A Blockchain Based System for Multi-level Log Storage

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7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering

作者： Xu, Gang Yun, Fan Liu, Xin Chen, Xiu-Bo School of Information Science and Technology North China University of Technology Beijing100144 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610025 China School of Information Engineering Inner Mongolia University of Science and Technology Baotou014010 China Information Security Center State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9781510656437

With the development of the Internet, many log data are generated in the server. How to ensure the security of log data in the server is a problem that needs to be solved. At present, log data storage in most servers relies on a centralized log database or local files. With the increase of running time and scale, there are great challenges in data security and stability. Meanwhile, attackers can tamper with the server's logs to evade tracking. To solve the above problems, this paper proposes a secure multi-level log data storage system based on permissioned blockchain and permission-less blockchain, with high throughput and low cost. In addition, different encryption methods are adopted for different levels of log data to provide higher security. © 2022 SPIE. All rights reserved.

关键词： Blockchain

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TL-KDNET: A Malicious Traffic Detection Model Integrating Transfer Learning and Knowledge Distillation

TL-KDNET: A Malicious Traffic Detection Model Integrating Tr...

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International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Kun Yu YuanYuan Huang Linshan Zhang Jun Lv Jiazhong Lu School of Cybersecurity (Xin Gu Industrial College Chengdu University of Information Technology Chengdu China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu China SUGON Industrial Control and Security Center Chengdu China MetaX Integrated Circuits (Shanghai) Co. Ltd

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

With the evolution of cyberattacks, traditional methods for malicious traffic detection face increasingly complex threats. To address this challenge, we propose a novel model, TL-KDNet, which integrates transfer learning, domain adaptation, and knowledge distillation techniques to enhance the accuracy and generalization capabilities of malicious traffic detection. In our model, KullbackLeibler (KL) divergence serves as a core module to quantify the differences in probability distributions between the source and target domains, thereby effectively facilitating knowledge transfer from known source domains to unknown target domains. Additionally, by leveraging Maximum Mean Discrepancy (MMD) and a single-sample distillation loss function, we achieve alignment of domain features and transfer of knowledge. Experimental results demonstrate that TL-KDNet improves detection accuracy by 4% on public datasets compared to existing methods.

关键词： Performance evaluation Adaptation models Accuracy Quantization (signal) Computational modeling Scalability Transfer learning Training data Real-time systems Probability distribution

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LOGNET: A Knowledge Distillation-Based Model for Log Anomaly Detection

LOGNET: A Knowledge Distillation-Based Model for Log Anomaly...

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International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Kun Yu Yuanyuan Huang Linshan Zhang Jun Lv Jiazhong Lu School of Cybersecurity (Xin Gu Industrial College) Chengdu University of Information Technology Chengdu China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu China SUGON Industrial Control and Security Center Chengdu China MetaX Integrated Circuits (Shanghai) Co. Ltd

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

Due to the diverse nature of attack forms, supervised log anomaly detection models require a large volume of labeled data, making the annotation process time-consuming and labor-intensive. Conversely, unsupervised log anomaly detection models often struggle to effectively capture log features and accurately identify anomalous logs. To address these challenges, this study constructs a collaborative learning framework and proposes the LogNet model. This framework enables mutual learning between two distinct models, enhancing their performance synergistically. In the LogNet model, log data is first converted into directed graphs, leveraging the strengths of Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT). Through collaborative learning, the two models guide each other, boosting their detection capabilities. The effectiveness of LogNet is validated on five public datasets, with experimental results demonstrating that LogNet significantly outperforms traditional models in malicious log detection, achieving up to a 4 % improvement in accuracy.

关键词： Knowledge engineering Solid modeling Accuracy Federated learning Graph convolutional networks Information processing Media Feature extraction Data models Anomaly detection

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GLADformer: A Mixed Perspective for Graph-level Anomaly Detection

arXiv

引用

arXiv 2024年

作者： Xu, Fan Wang, Nan Wu, Hao Wen, Xuezhi Zhang, Dalin Lu, Siyang Li, Binyong Gong, Wei Wan, Hai Zhao, Xibin School of Software Engineering Beijing Jiaotong University Beijing China University of Science and Technology of China Hefei China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu China BNRist KLISS School of Software Tsinghua University Beijing China

Graph-Level Anomaly Detection (GLAD) aims to distinguish anomalous graphs within a graph data set. However, current methods are constrained by their receptive fields, struggling to learn global features within the graphs. Moreover, most these methods are based on spatial domain and lack exploration of spectral characteristics. In this paper, we propose a multi-perspective hybrid graph-level anomaly detector named GLADformer, consisting of two key modules. Specifically, we first design a Graph Transformer module with global spectrum enhancement, which ensures balanced and resilient parameter distributions by fusing global features and spectral distribution characteristics. Furthermore, to explore local anomalous attributes, we customize a band-pass spectral GNN message passing module that enhances the model’s generalization capability. Through comprehensive experiments on ten real-world datasets from multiple domains, we validate the effectiveness and robustness of GLADformer. This demonstrates that GLADformer outperforms current state-of-the-art graph-level anomaly detection methods, particularly in effectively capturing global anomaly representations and spectral characteristics. Copyright © 2024, The Authors. All rights reserved.

关键词： Distribution transformers

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Analysis and Improvement of a Conditional Privacy-Preserving Authentication Scheme with Double-Insurance in Vanets

SSRN

引用

SSRN 2023年

作者： Wen, Jiaming Bai, Lu Liu, Jinhui Zhang, Huanguo Wang, Houzhen Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan430072 China School of Cyber Security Northwestern Polytechnical University Xi’an710072 China State Key Laboratory of Cryptology Beijing100878 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610025 China

As the intelligent transportation system develops by leaps and bounds, there is an increasing interest in the potential of Vehicular Ad-hoc Networks (VANETs) to enhance traffic efficiency and safety. The Conditional Privacy-Preserving Authentication (CPPA) scheme, which is suitable for solving anonymous authentication and message integrity during the communication, has been extensively studied to build the first line of defense for a secure VANET system. However, most existing Identitybased CPPA schemes rely on Tamper-Proof Devices (TPDs), which are susceptible to side-channel attacks. If the sensitive parameters stored in TPDs are compromised, the system could be completely ruined. In order to address this issue, Xiong et al. introduced a CPPA scheme with double-insurance, which was claimed to be secure against adaptively chosen message attacks. Unfortunately, we show in this paper that the scheme is universally forgeable, meaning that anyone can create a valid signature on any message, rendering the scheme inadequate for conditional privacy-preserving. After analyzing their shortcomings, we present a new scheme that addresses the issue, and formally proves its security under the Discrete Logarithms (DL) and Decisional Diffie-Hellman (DDH) assumptions. We also conducted experiments and simulations to evaluate our proposed scheme. The results demonstrate itssuperiority over similar schemes, indicating it is practical and effective in real-world scenarios. © 2023, The Authors. All rights reserved.

关键词： Vehicular ad hoc networks

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Several new classes of optimal ternary cyclic codes with two or three zeros

arXiv

引用

arXiv 2024年

作者： Wu, Gaofei You, Zhuohui Zha, Zhengbang Zhang, Yuqing The State Key Laboratory of Integrated Service Networks School of Cyber Engineering Xidian University Xi’an710071 China The Advanced Cryptography System Security Key Laboratory of Sichuan Province China The College of Mathematics and System Sciences Xinjiang University Urumqi830046 China The National Computer Network Intrusion Protection Center University of Chinese Academy of Sciences Beijing101408 China

Cyclic codes are a subclass of linear codes and have wide applications in data storage systems, communication systems and consumer electronics due to their efficient encoding and decoding algorithms. Let α be a generator of F∗3m, where m is a positive integer. Denote by C(i1,i2,···,it) the cyclic code with generator polynomial mαi1 (x)mαi2 (x) · · · mαit (x), where mαi(x) is the minimal polynomial of αi over F3. In this paper, by analyzing the solutions of certain equations over finite fields, we present four classes of optimal ternary cyclic codes C(0,1,e) and C(1,e,s) with parameters [3m − 1, 3m − 3m/2 − 2, 4], where s = 3m−1/2 . In addition, by determining the solutions of certain equations and analyzing the irreducible factors of certain polynomials over F3m, we present four classes of optimal ternary cyclic codes C(2,e) and C(1,e) with parameters [3m−1, 3m−2m−1, 4]. We show that our new optimal cyclic codes are inequivalent to the known ones. Copyright © 2024, The Authors. All rights reserved.

关键词： Polynomials

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