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2023 IEEE International Conference on E-Health networking, Application and Services, Healthcom 2023

作者： Li, Ta Tian, Youliang Xiong, Jinbo Wang, Linjie College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China College of Computer and Cyber Security Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal University Fuzhou350117 China School of Data Science Tongren University Tongren554300 China

ISBN: (纸本)9798350302301

The swift progression of the Internet of Things and the extensive integration of machine learning have spurred the growth of intelligent healthcare. Many intelligent healthcare devices, limited by their own computing and storage resources, require outsourcing data analysis tasks to cloud platforms for efficient and accurate results. Unfortunately, malicious cloud services lead to privacy breaches in outsourced data and untrustworthiness in learning models. To address these challenges, this paper proposes a verifiable privacy-preserving outsourced prediction scheme based on blockchain in smart healthcare (VPOL). Specifically, by incorporating blockchain technology into VPOL, we build a robust and scalable framework to prevent falsification of outsourced data and learning models in a decentralized and transparent manner. Then, we design a training committee approach to ensure the reliability of outsourced prediction and employ homomorphic encryption and commitment scheme to protect the privacy and integrity of the data. Finally, theoretical analysis proves the effectiveness and security of VPOL. Sufficient experiments demonstrate that VPOL achieves the approximate accuracy of the plaintext. © 2023 IEEE.

关键词： Blockchain

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A Lightweight Chinese Summarization Generation Method Based on Knowledge Distillation 4

A Lightweight Chinese Summarization Generation Method Based ...

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4th International Conference on information Science, Parallel and Distributed Systems, ISPDS 2023

作者： Liu, Shiyue Xiao, Yannan Wang, Xueyong Huang, Wenming Deng, Zhenrong School of Computer and Information Security Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China School of Computer and Information Security Guilin University of Electronic Technology Guilin China Guilin Xintong Technology Co. Guilin China

ISBN: (纸本)9798350337181

In recent years, large-scale pretrained natural language processing models such as BERT, GPT3 have achieved good results in processing tasks. However, in daily applications, these large-scale language models usually exist large model size or long running time, and the model portability and application are not very convenient. To settle this problem, we come up with a lightweight summarization generation method based on knowledge distillation (LW-BERT-KD, abbreviated as LBK model) to prove that complex neural network knowledge (taking BERT model as an example) can be extracted to lightweight language processing models (taking BiLSTM model as an example), thus achieving the purpose of complex knowledge extraction and model compression. In this paper, the BERT model is regarded as the teacher model, and the BiLSTM is regarded as the student model. The knowledge distillation technology is the transfer of knowledge from teacher models to student models, so that the generated model is effective, lightweight, and easy to port. On the Chinese abstract generation dataset, the training effect of this model is significantly better than the basic Transformer baseline. The experimental results show that if the training parameters are increased by 100 times, the model achieves comparable results with complex preprocessing language models. © 2023 IEEE.

关键词： Distillation

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FastIoTBot: Identifying IoT Bots by Fast Detecting Anomalous Domain Queries with Long Short-Term Memory networks 3

FastIoTBot: Identifying IoT Bots by Fast Detecting Anomalous...

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3rd International Conference on Consumer Electronics and computer Engineering, ICCECE 2023

作者： Li, Ruyu Yin, Lihua Zhang, Yuanfei Qian, Kexiang Luo, Xi School of Cyberspace Security Guangzhou University Guangzhou China Peng Cheng Laboratory Shenzhen518038 China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Institute of Information and Telecommunication of Global Energy Interconnection Research Institute State Grid Key Laboratory of Information and Network Security Beijing China

ISBN: (纸本)9798350331578

Along with the progression in technology, Internet of Things (IoT) has been dramatically developed in recent ten years. It connects physical world and digital world, which makes people's life more convenient. However, IoT devices have bring great vulnerability to Internet security since they usually under weak protection, which makes them easy to be exploited by criminals to launch multiple attacks. In fact, IoT devices have been a crucial part of botnets that launch horrible Distributed Denial of Service (DDoS) with explosive traffic. Unfortunately, traditional detection works have limited effectiveness face IoT botnets because of the restricted resources of IoT devices and unprecedented huge scale of IoT botnets. To mitigate the threat of IoT botnets, in this paper, we propose a lightweight system, named FastIoTBot, to discover compromised IoT devices in a fast way. FastIoTBot can distinguish compromised IoT devices instantly and prevent potential malicious behaviors by examining domain query activities. Specifically, FastIoTBot monitors the DNS query for a device and generates its NXDOMAIN query sequence. Then, for each domain in the sequence, FastIoTBot takes the domain name string as input and calculates its malicious score using long short-term memory (LSTM) model. Finally, FastIoTBot identifies compromised IoT devices through analyzing NXDOMAIN sequences with internal domains' malicious score leveraging threshold random walk (TRW) algorithm. The effectiveness of FastIoTBot is evaluate with real world DNS data of two large ISP networks. The results show that FastIoTBot perform well with over 99% accuracy. © 2023 IEEE.

关键词： Long short-term memory

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Monitoring method of API encryption parameter tamper attack based on deep learning 6

Monitoring method of API encryption parameter tamper attack ...

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6th International Conference on Intelligent Computing, Communication, and Devices, ICCD 2023

作者： Chen, Lu Zhang, Tao Ma, Yuanyuan Chen, Mu State Grid Smart Grid Research Institute Co. Ltd Nanjing210003 China State Grid Key Laboratory of Information and Network Security Nanjing210003 China

ISBN: (数字)9781510666269

ISBN: (纸本)9781510666252

This paper proposes an abnormal behavior detection technology of power 5G terminal based on the characteristics of business interaction mode. First, the abnormal behavior detection framework of power 5G terminal is designed. Secondly, considering various security factors of the basic operating environment, a 5G security monitoring indicator system is established around the terminal side security indicators, network side security indicators and API side security indicators. Then, study the automatic learning and construction technology of normal behavior benchmark model, establish a normal benchmark model based on multiple RBM neural networks, study the normal behavior baseline of 5G terminal operation behavior benchmark, the same type of equipment benchmark, specific communication line benchmark and other normal behavior baselines, and build the security baseline indicators of the normal behavior model of 5G terminal. On this basis, abnormal behavior detection technology based on normal benchmark model is studied. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Cryptography

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A Frequent Subgraph Publishing Algorithm Based on Differential Privacy

A Frequent Subgraph Publishing Algorithm Based on Differenti...

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2022 International Conference on Cloud Computing, Big Data Applications and Software Engineering, CBASE 2022

作者： Liu, Wenfen Chen, Di Yu, MingHao Xu, Qiang Guilin University of Electronic Technology School of Computer and Information Security Guilin Guangxi China Guangxi Key Laboratory of Cryptography and Information Security Guilin Guangxi China

ISBN: (纸本)9798350396393

Frequent subgraph mining is a common technology in data mining and has important application value. However, If graph data contains private information, publishing frequent subgraphs and support directly will lead to privacy leakage. Aiming at the problem of lower availability of the published results of the existing differential privacy frequent subgraphs publishing algorithms, this paper proposes a frequent subgraphs publishing algorithm based on differential privacy (DPFSG). First, in order to improve the availability of frequent subgraphs, we design an improved frequent subgraphs preprocessing strategy based on exponential mechanism. We reduce the scale of candidate subgraphs based on threshold, and mine frequent subgraphs combined with exponential mechanism. Secondly, in order to improve the availability of the published results of the frequent subgraphs support, we combine the hierarchical clustering method to cluster and group the support of each frequent subgraphs, and then use the Laplace mechanism to add noise to each group. Theoretical analysis and experimental results verify that the method improves the availability of published data while preserving privacy. © 2022 IEEE.

关键词： Publishing

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Automatic skin lesion classification using a novel densely connected convolutional network integrated with an attention module

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 8943-8956页

作者： Shan, Pufang Chen, Jialei Fu, Chong Cao, Lin Tie, Ming Sham, Chiu-Wing School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China Science and Technology on Space Physics Laboratory Beijing100076 China School of Computer Science The University of Auckland Auckland New Zealand

Automated skin lesion classification in dermoscopy images remains challenging due to the existence of artefacts and intrinsic cutaneous features, diversity of lesion morphology, insufficiency of training data, and class imbalance problem. To address these challenges, we propose a new densely connected convolutional network termed AttDenseNet-121, which is obtained by integrating the convolutional block attention module (CBAM) into DenseNet-121. CBAM is a simple yet efficient attention module, which is further improved by adding a novel pooling layer (multiscale-pooling) to effectively enhance its attention capability. The optimized CBAM strengthens the representation power of DenseNet-121 by emphasizing meaningful features and suppressing unnecessary ones, thus significantly enhancing the classification performance. Besides, to handle the imbalanced dataset, we employ an improved focal loss function rather than the traditional cross-entropy loss function to train AttDenseNet-121. The improved focal loss is calculated from a different perspective compared with the original focal loss. It makes the distribution of positive samples and negative samples in each batch the same as that in the original data, successfully mitigating the negative influence of class imbalance on this multi-class classification task. We conduct extensive experiments on the public benchmark dataset (HAM10000) and the results indicate that our proposed method achieves superior performance compared with that of the baselines and state-of-the-art algorithms. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolution

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CASSOR: Class-Aware Sample Selection for Ordinal Regression with Noisy Labels 20th

CASSOR: Class-Aware Sample Selection for Ordinal Regression...

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20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Yuan, Yue Wan, Sheng Zhang, Chuang Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing China Jiangsu Key Laboratory of Image and Video Understanding for Social Security Nanjing China

ISBN: (纸本)9789819970216

Ordinal regression aims at solving the classification problem, where the categories are related in a natural order. Due to the difficulty in distinguishing between highly relevant categories, label noise is frequently present in ordinal data. Moreover, the varying degrees of relevance between categories can lead to an inconsistent distribution of misclassification loss across categories, posing a challenge to select clean data consistently from all categories for training. To overcome this limitation, we develop a sample selection method termed ‘Class-Aware Sample Selection for Ordinal Regression’ (CASSOR). To be concrete, we devise a class-specific sample selection strategy in order to adaptively acquire sufficient clean examples for robust model training. Moreover, a label-ranking regularizer is designed to help guide the sample selection process via exploring the ordinal relationship between different examples. As a result, our proposed CASSOR is endowed with strong discrimination abilities on ordinal data. Intensive experiments have been performed on multiple real-world ordinal regression datasets, which firmly demonstrates the effectiveness of our method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Regression analysis

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A High-Quality and Efficient Bus-Aware Global Router

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Chinese Journal of Electronics 2025年第2期34卷 444-456页

作者： Liu Genggeng Wei Ling Yu Yantao Xu Ning College of Computer and Data Science Fuzhou University Fuzhou 350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou 350108 China School of Information Engineering Wuhan University of Technology Wuhan 430070 China

As advanced technology nodes enter the nanometer era, the complexity of integrated circuit design is increasing, and the proportion of bus in the net is also increasing. The bus routing has become a key factor affecting the performance of the chip. In addition, the existing research does not distinguish between bus and non-bus in the complete global routing process, which directly leads to the expansion of bus deviation and the degradation of chip performance. In order to solve these problems, we propose a high-quality and efficient bus-aware global router, which includes the following key strategies: By introducing the routing density graph, we propose a routing model that can simultaneously consider the routability of non-bus and the deviation value of bus;A dynamic routing resource adjustment algorithm is proposed to optimize the bus deviation and wirelength simultaneously, which can effectively reduce the bus deviation;We propose a layer assignment algorithm consider deviation to significantly reduce the bus deviation of the 3D routing solution;And a depth-first search (DFS)-based algorithm is proposed to obtain multiple routing solutions, from which the routing result with the lowest deviation is selected. Experimental results show that the proposed algorithms can effectively reduce bus deviation compared with the existing algorithms, so as to obtain high-quality 2D and 3D routing solutions considering bus deviation.

关键词： Global routing Layer assignment Bus deviation Physical design Bus routing

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Abnormal behavior detection technology of power 5G terminal based on the characteristics of service interaction mode 6

Abnormal behavior detection technology of power 5G terminal ...

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6th International Conference on Intelligent Computing, Communication, and Devices, ICCD 2023

作者： Chen, Lu Li, Nige Chen, Mu Dai, Zaojian Li, Yong State Grid Smart Grid Research Institute Co. Ltd Nanjing210003 China State Grid Key Laboratory of Information and Network Security Nanjing210003 China

ISBN: (数字)9781510666269

ISBN: (纸本)9781510666252

关键词： network security

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Interference Coordination Technology of Ultra Dense Deployment Based on Clustering Algorithm 5

Interference Coordination Technology of Ultra Dense Deployme...

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5th IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2023

作者： Wang, Haoyuan Tian, Yue Li, Wenda Xiamen University of Technology Fujian Key Laboratory of Communication Network and Information Processing Xiamen China University College London Department of Security and Crime Science London United Kingdom

ISBN: (纸本)9798350333442

A clustering algorithm based Ultra Dense Deployment is proposed to address the problem of establishing a reasonable interference model and allocating ABS reasonably to minimize interference and maximize throughput in Ultra Dense Deployment. By analyzing the distribution pattern of low-power nodes in Ultra Dense Deployment, the interference generated by the cluster center is used to replace the interference of other nodes in the class. According to the principle of interference minimization, a distributed allocation scheme for ABS time slots based on K-means clustering is proposed, and a polling algorithm is used for user scheduling. Simulation results show that compared with the generalized interference model and traditional binary models, the interference calculation is more accurate, The throughput of the system is also higher. © 2023 IEEE.

关键词： K-means clustering

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