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10th IEEE Smart World Congress, SWC 2024

作者： Li, Jiayu Hu, Yuke Li, Xiaoguang Zhou, Shiqi Wang, Yuxiang Li, Fenghua Niu, Ben Institute of Information Engineering Chinese Academy of Sciences China University of Chinese Academy of Sciences School of Cyber Security China Key Laboratory of Cyberspace Security Defense China Zhejiang University The State Key Laboratory of Blockchain and Data Security China Xidian University School of Cyber Engineering China

ISBN: (纸本)9798331520861

Local differential privacy (LDP) provides a solution such that an untrusted server can achieve privacy-preserving data analysis. Node degree is one of the most fundamental metrics in graph data analysis. Existing LDP-based methods can not balance degree distribution estimation and degree distribution characteristic estimation. In this paper, we propose a local private estimator for degree distribution with Laplacian mechanism (LEDL), which uses elastic net regression to estimate the degree distribution, selects the regularization weight to prevent overfitting or underfitting. We can aslo adopt the existing method to calculate degree distribution characteristics according to data with Laplacian noise. Experiments demonstrate the effectiveness of LEDL in degree distribution and Laplacian mechanism in degree distribution characteristic estimation, six metrics are applied across four datasets. © 2024 IEEE.

关键词： Differential privacy

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Virtual Petrochemical FireFighting Training System Design and Implementation 14th

Virtual Petrochemical FireFighting Training System Design an...

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14th International Conference on Frontier Computing, FC 2024

作者： Li, Hengdong Ye, Jun Zhang, Dian Trusted and Intelligent Data Analytic Research Group School of Cyberspace Security Hainan University Haikou China Key Laboratory of Internet Information Retrieval of Hainan Province Hainan University Haikou China

ISBN: (纸本)9789819627974

Due to the special and dangerous nature of flammable and explosive petrochemical substances, petrochemical storage and transportation involves great risks and is highly prone to fire and explosion and other accidents, which poses a great challenge to firefighting and rescue. Due to the special characteristics of petrochemical fire accidents, traditional fire training and experience often cannot cope with them, resulting in low firefighting efficiency and heavy economic losses. With the wide application of virtual reality technology in the training field, this study summarises the current research status in the field of virtual reality firefighting, optimises the LOD technology, and develops a petrochemical firefighting training system based on virtual reality, and the research results effectively address the current shortcomings in petrochemical firefighting training. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fire extinguishers

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sSecure Net: A Hybrid CNN-LSTM-based Intrusion Detection System for Securing IoT Networks 25

sSecure Net: A Hybrid CNN-LSTM-based Intrusion Detection Sys...

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Proceedings of the 4th International Conference on Computer, Artificial Intelligence and Control Engineering

作者： Ovi Abul Hussain Zigang Chen Haihua Zhu Chongqing Key Laboratory of Cyberspace Security Monitoring and Governance & Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9798400712647

The widespread deployment of IoT devices has seen the emergence of a huge security threat, and IoT networks are the favorite targets for numerous cyberattacks. Due to the scarce resources in most IoT devices, the conventional intrusion detection systems (IDS) are unable to catch up with the enormous traffic in IoT networks. This paper suggests a new intrusion detection model based on CNN-LSTM, incorporating deep learning and statistical filtering to increase the intrusion detection in IoT networks. The model uses Convolutional Neural Networks (CNN) to capture the spatial relationships and Long Short-Term Memory (LSTM) networks to capture the temporal relationships in IoT network traffic. The model also uses statistical filtering techniques, including Median Filtering and Standard Deviation-based Filtering, to pre-process the traffic and eliminate the noise and the outliers, making the model's precision improved. The model is tested based on the Edge-IIoTset dataset, comprised of different diverse attack types, including Denial of Service (DoS), SQL injection, Ransomware, and Man-in-the-Middle (MITM). The model gives 94.99% precision, reflecting the model's precision in detecting different intrusions. This paper sets the efficacy in the use of the integration between the use of CNN-LSTM and statistical filtering to build an efficient, scalable, and precise intrusion detection model in IoT networks. The findings offer a real-time solution to increase IoT security, in particular, intrusion detection in IoT networks in real-time.

关键词： Anomaly Detection

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Anatomical Prior Guided Spatial Contrastive Learning for Few-Shot Medical Image Segmentation 24

Anatomical Prior Guided Spatial Contrastive Learning for Few...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Huang, Wendong Hu, Jinwu Bi, Xiuli Xiao, Bin Chongqing Key Laboratory of Image Cognition Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Post and Telecommunications Chongqing China Pazhou Lab School of Software Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798400706868

Few-shot semantic segmentation has considerable potential for low-data scenarios, especially for medical images that require expert-level dense annotations. Existing few-shot medical image segmentation methods strive to deal with the task by means of prototype learning. However, this scheme relies on support prototypes to guide the segmentation of query images, ignoring the rich anatomical prior knowledge in medical images, which hinders effective feature enhancement for medical images. In this paper, we propose an anatomical prior guided spatial contrastive learning, called APSCL, which exploits anatomical prior knowledge derived from medical images to construct contrastive learning from a spatial perspective for few-shot medical image segmentation. The new framework forces the model to learn the features in line with the embedded anatomical representations. Besides, to fully exploit the guidance information of the support samples, we design a mutual guidance decoder to predict the label of each pixel in the query image. Furthermore, our APSCL can be trained end-to-end in the form of episodic training. Comprehensive experiments on three challenging medical image datasets, i.e., CHAOS-T2, MS-CMRSeg, and Synapse, prove that our method significantly surpasses state-of-the-art few-shot medical segmentation methods, with a mean improvement of 3.61%, 2.30%, and 6.38% on the Dice score, respectively. © 2024 ACM.

关键词： Contrastive Learning

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PriFU: Capturing Task-Relevant Information Without Adversarial Learning 24

PriFU: Capturing Task-Relevant Information Without Adversari...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Bi, Xiuli Hu, Yang Liu, Bo Li, Weisheng Cosman, Pamela Xiao, Bin Chongqing Key Laboratory of Image Cognition Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Posts and Telecommunications Chongqing China Department of Electrical and Computer Engineering University of California San Diego San Diego United States

ISBN: (纸本)9798400706868

As machine learning advances, machine learning as a service (MLaaS) in the cloud brings convenience to human lives but also privacy risks, as powerful neural networks used for generation, classification or other tasks can also become privacy snoopers. This motivates privacy preservation in the inference phase. Many approaches for preserving privacy in the inference phase introduce multi-objective functions, training models to remove specific private information from users' uploaded data. Although effective, these adversarial learning-based approaches suffer not only from convergence difficulties, but also from limited generalization beyond the specific privacy for which they are trained. To address these issues, we propose a method for privacy preservation in the inference phase by removing task-irrelevant information, which requires no knowledge of the privacy attacks nor introduction of adversarial learning. Specifically, we introduce a metric to distinguish task-irrelevant information from task-relevant information, and achieve more efficient metric estimation to remove task-irrelevant features. The experiments demonstrate the potential of our method in several tasks. Our code will be available at: https://***/iwhoyoung/PriFU. © 2024 ACM.

关键词： Adversarial machine learning

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Federated Hybrid-Supervised Learning for Universal Medical Image Segmentation

Federated Hybrid-Supervised Learning for Universal Medical I...

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

作者： Zheng, Shenhai Wen, Sian Li, Congyu Chen, Qing Li, Laquan College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing China School of Science Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9798350368741

Federated Learning (FL) is an advanced technology that tackles the challenge of blocked data arising from privacy concerns, enabling the training of deep learning models without the need for data sharing. However, FL faces difficulties with heterogeneous data and limited annotations in medical image segmentation. Motivated by this discovery, this study proposes a novel hybrid-supervised federated learning method (FedSLAG) that explores various types of annotations in medical imaging. To focus more on weakly-supervised and unsupervised scenarios within hybrid-supervised learning, a federated Gaussian enhancement module was proposed for heterogeneous sparse annotations (points and scribbles). The feature extraction module combines the features of multiple weakly-supervised clients and establishes the correlation of similar pixels, thus making up for the deficiency of scarce annotations and the insufficient feature extraction capability of a single machine. Then, a two-stage broadcast mechanism based on supervision sparsity was proposed to alleviate optimization deviation in local models. Experiments on breast tumor and skin lesion segmentation tasks demonstrate significant efficiency gains as well as highly competitive segmentation accuracy in many hybrid-supervised situations. Our codes are available at: https://***/TrivenDev/FedSLAG. © 2025 IEEE.

关键词： federated learning hybrid-supervised learning medical image segmentation

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Identifying data Breaches in Dark Web through Prompt Active Learning 9

Identifying Data Breaches in Dark Web through Prompt Active ...

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9th IEEE International Conference on data Science in cyberspace, DSC 2024

作者： Xiang, Hui Wu, Yifan Wang, Xuebin Shi, Jinqiao Zhao, Can Zhao, Jiapeng Beijing University of Posts and Telecommunications School of Cyberspace Security Beijing China Zhejiang University The State Key Laboratory of Blockchain and Data Security Hangzhou China Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China Chinese Academy of Sciences Institute of Information Engineering Beijing China

ISBN: (纸本)9798350391367

Frequent data breaches have attracted people's attention. The dark web forums have become the crucial platform for data breach transactions. While some forums are well-organized with distinct sections, such as a dedicated area for leaks where most threads are related to data breaches, others lack clear segmentation, like Fear Forum. Hackers conduct a variety of discussions there so it is filled with tons of non-data breach information and building a data breach dataset for a classifier requires huge human resources. The content of these threads is diverse and complex, spanning various fields, and containing specific terminology in the dark web. Identifying data breach threads in these forums is extremely challenging for the model. With the influx of new threads every day the static model trained on old data may not perform well when processing new data, which also brings serious difficulty to identification. To address these challenges, we first design Prompt-based Active Learning (PAL) to guide the model to identify through prompts and select high-quality samples for annotation. Then we propose a system that combines PAL and DarkBERT. Our system can not only greatly reduce annotation costs but also continuously learn and update itself with the new informative data selected by PAL. Through rich experiments on a data breach dataset, we demonstrate that our system effectively identifies breach threads and PAL significantly outperforms other baselines. We also search for the optimal prompt templates to improve system performance. © 2024 IEEE.

关键词： Active learning

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A Feature Fusion Network with Multiscale Adaptively Attentional for Object Detection in Complex Traffic Scenes

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年

作者： Cai, Fei Qu, Zhong Yin, Xuehui Chongqing Key Laboratory of Computational Intelligence The Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education The Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China

Currently, small object detection in complex traffic scenarios remains a major challenge due to variations in object shape, scales, and external environment, as well as a high inter-class similarity. Although the latest algorithms achieve high accuracy, the results for small objects are still unsatisfactory. To obtain satisfactory small object detection results, the YOLOv8 algorithm is used to construct a feature fusion network with multiscale adaptively attentional (MAAFFNet). Firstly, the C2f module is modified to replace the simple summation operation with Multiscale Iterative Attentional Feature Fusion (MSIAFF), which enhances the ability of multi-scale aggregation of the C2f module and suppresses the interference of irrelevant information, resulting in a richer semantic feature map. Secondly, the adaptive spatial feature fusion (ASFF) module is introduced to deal with the feature maps of different scales, allocate the weights of spatial features of different levels, and enhance the consistency of semantic information between each layer of the feature maps, so as to improve the detail richness of the network. Finally, to achieve more accurate classification of small objects, a Slide loss function is introduced to achieve object classification and localization. Our proposed algorithm has been extensively experimented on KITTI, COCO-traffic and VOC 2012 datasets, respectively, comparing the proposed method to popular two-stage and single-stage object detection algorithms, the proposed method performs well in terms of accuracy. © 2024 IEEE.

关键词： Object recognition

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CoGAP: A Personalized Federated Learning Method Using Collaborative Optimization for Medical Image Classification

CoGAP: A Personalized Federated Learning Method Using Collab...

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

作者： Zheng, Shenhai Li, Congyu Wen, Sian Gao, Xi Yu, Lei College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing China Emergency Department The Second Affiliated Hospital of Chongqing Medical University Chongqing China

ISBN: (纸本)9798350368741

Federated learning (FL) has been widely used in medical image processing to protect data privacy, but it has issues with data heterogeneity. Personalized federated learning have emerged to tackle these issues but often focuses too much on personalized models at the expense of global models. To address these problem, we propose a personalized federated learning method CoGAP, using collaborative optimization to enhance both personalized and global model performance. On the client side, it employs adaptive weight aggregation to initialize personalized models and uses a training strategy based on L2 regularization. On the server side, it implements a gradient accumulation-based aggregation method. These modules facilitate a collaborative optimization process where the global model guides the personalized models, and the personalized models provide positive feedback to the global model, ultimately leading to mutual improvement for both. We conducted extensive comparative experiments on the OCT2017 dataset, as well as the BloodMnist and PathMnist (subsets from MedMnist), to evaluate both global and personalized models. Results show that CoGAP own a unique strength which not only outperforms other personalized federated learning methods in personalization capability while also remarkably achieving satisfying global generalization. Code is available at https://***/lcyCQUPT/CoGAP. © 2025 IEEE.

关键词： collaborative optimization federated learning medical image classification

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Block-gram: Mining Knowledgeable Features for Smart Contract Vulnerability Detection 7th

Block-gram: Mining Knowledgeable Features for Smart Contrac...

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7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Li, Tao Wang, Haolong Fang, Yaozheng Jian, Zhaolong Wang, Zichun Xie, Xueshuo Tianjin Key Laboratory of Network and Data Security Technology Tianjin China College of Computer Science Nankai University Tianjin China Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province Hangzhou China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031281235

Effective vulnerability detection of large-scale smart contracts is critical because smart contract attacks frequently bring about tremendous economic loss. However, code analysis requiring traversal paths and learning methods requiring many features training is too time-consuming to detect large-scale on-chain contracts. This paper focuses on improving detection efficiency by reducing the dimension of the features, combined with expert knowledge. We propose a feature extraction method Block-gram to form low-dimensional knowledgeable features from the bytecode. We first separate the metadata and convert the runtime code to opcode sequence, dividing the opcode sequence into segments according to some instructions (jump, etc.). Then, we mine extensible Block-gram features for learning-based model training, consisting of 4-dimensional block features and 8-dimensional attribute features. We evaluate these knowledge-based features using seven state-of-the-art learning algorithms to show that the average detection latency speeds up 25 to 650 times, compared with the features extracted by N-gram. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart contract

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