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

作者： Wang Gefeng Zhang Shibin Yan Lili Lv Zhiying Chang Yan Qin Zhi 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

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

Clustering is a type of data mining algorithm aimed at discovering hidden patterns in the data, and spatial clustering is an important type of it. Density-based spatial clustering of applications with noise (DBSCAN) is a clustering algorithm that can discover clusters of arbitrary shapes and has robustness to noise. DBSCAN has been widely studied and applied due to its superior clustering quality, but its high complexity limits its performance on massive-scale datasets. This paper proposes a sampling-based grid-structured DBSCAN algorithm with linear complexity that can be processed in parallel. The algorithm first clusters the sampled dataset to discover the main features of the dataset; then it establishes grids and indexes to accelerate neighbor point queries; finally, through specially designed decoupled algorithm structure, it achieves lock-free parallel computation. Experiments show that this algorithm outperforms the state-of-the-art DBSCAN algorithm under various tested datasets and parameter conditions.

关键词： Shape Noise Clustering algorithms Information processing Media Spatial databases Robustness Complexity theory Indexes Time complexity

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A Method for Compressed Storage and Retrieval of Triplet Data Based on Cuckoo Hashing

A Method for Compressed Storage and Retrieval of Triplet Dat...

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

作者： Liu Pengcheng Qinzhi Zhang Shibin Chang Yan Yan Lili 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

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

This paper proposes a method for compressing and retrieving triple data based on optimized cuckoo hash. Firstly, the optimized cuckoo hash algorithm is utilized to quickly establish node indexes. Then, the relationship information of triples is stored in the form of a sparse matrix according to the relationship type. Finally, the double compression algorithm k 2 -Rel is employed to compress the relationship attribute matrix of stored triple information, aiming to reduce storage space and lower storage costs. Experimental results demonstrate that the optimized cuckoo hash algorithm significantly reduces the time for building node indexes. Furthermore, utilizing the k2-Rel compression algorithm for compressing the relationship information between nodes substantially decreases storage space, ultimately achieving efficient storage and retrieval of triple data.

关键词： Costs Triples (Data structure) Buildings Information processing Media Motion pictures Sparse matrices Indexes Compression algorithms

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Research of Drawing Data Asset Graph Based on Force-Oriented Algorithm

Research of Drawing Data Asset Graph Based on Force-Oriented...

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

作者： Huang Sihan Qin Zhi Zhang Shibin Chang Yan Yan Lili 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

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

With the rapid development of the new energy vehicle industry, the data volume in the industry is growing explosively. How to effectively visualize and analyze these data has become a focus of attention in the industry. As an intuitive way of data organization and presentation, asset graphs can display complex data relationships in a graphical form, which helps to quickly understand the information behind the data. This paper proposes a method for drawing data asset graphs based on the force-oriented algorithm, specifically tailored to the data characteristics of the new energy vehicle industry. The method improves the existing force-oriented algorithms through feature-enhanced node importance assessment, customized force model construction, multi-level community division and clustering, and node layout optimization. Experimental verification has shown that this method can effectively visualize new energy vehicle data, with certain advantages in layout quality and layout efficiency.

关键词： Industries Runtime Layout Force Clustering algorithms Data visualization Organizations Media Optimization Research and development

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A Secure Multiparty Quantum Homomorphic Encryption Scheme

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Computers, Materials & Continua 2022年第11期73卷 2835-2848页

作者： Jing-Wen Zhang Xiu-Bo Chen Gang Xu Heng-Ji Li Ya-Lan Wang Li-Hua Miao Yi-Xian Yang Information Security Center State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China School of Information Science and Technology North China University of TechnologyBeijing100144China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Sichuan610025China Quantum Technology Lab&Applied Mechanics Group University of MilanMilan20133Italy Department of Computer Science Faculty of Engineering and Physical SciencesUniversity of SurreyGuildfordSurreyGU27XHUnited Kingdom Huawei Technologies Co.Ltd Shenzhen518129China

The significant advantage of the quantum homomorphic encryption scheme is to ensure the perfect security of quantum private *** this paper,a novel secure multiparty quantum homomorphic encryption scheme is proposed,which can complete arbitrary quantum computation on the private data of multiple clients without decryption by an almost dishonest ***,each client obtains a secure encryption key through the measurement device independent quantum key distribution protocol and encrypts the private data by using the encryption operator and ***,with the help of the almost dishonest server,the non-maximally entangled states are preshared between the client and the server to correct errors in the homomorphic evaluation of T gates,so as to realize universal quantum circuit evaluation on encrypted ***,from the perspective of the application scenario of secure multi-party computation,this work is based on the probabilistic quantum homomorphic encryption scheme,allowing multiple parties to delegate the server to perform the secure homomorphic *** operation and the permission to access the data performed by the client and the server are clearly pointed ***,a concrete security analysis shows that the proposed multiparty quantum homomorphic encryption scheme can securely resist outside and inside attacks.

关键词： Quantum homomorphic encryption secure multiparty computation almost dishonest server security

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Petri net-based illegal information flow detection method for node-chain access control model 23

Petri net-based illegal information flow detection method fo...

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Proceedings of the 2023 International Conference on Communication Network and Machine Learning

作者： Binyong Li Jie Fang Xianhui Deng Yuhan Sheng School of Cybersecurity (Xin Gu Industrial College) Chengdu University of Information Technology China and Advanced Cryptography and System Security Key Laboratory of Sichuan Province China Sichuan Institute of Computer Sciences China School of Cybersecurity Chengdu University of Information Technology China

ISBN: (纸本)9798400716683

To address the problem of illegal information flow in the node chain access control model constructed by the existing attribute-based access control (ABAC) model when applied to a large-scale node chain scenario with more than 100 nodes, an illegal information flow detection method is proposed for the node chain access control model based on the classical Petri net. The method introduces the reachable identity and reachable identity graph of the classical single-Token Petri net, which can analyze the illegal information flow in the ABAC-based node-chain access control model conveniently and visually. Meanwhile, it can effectively use the reachable identity state in the Petri net to detect the illegal information flow in the model. The case analysis shows that the method can effectively detect the illegal information flow appearing in the model.

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MC-GAN: An Adversarial Sample Defense Algorithm

MC-GAN: An Adversarial Sample Defense Algorithm

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

作者： WANG CHENLI WU JUYANG YANG XING WANG JUNFEI SHU JIAN LU JIAZHONG HUANG YUANYUAN HAN SHUO School of Cybersecurity Chengdu University of Information Technology Chengdu Sichuan China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu Sichuan China Department of Statistics and Data Sciences Northwestern University Evanston IL USA

The current adversarial sample defense algorithms are plagued by issues such as poor defense effectiveness and high training costs. In order to enhance sample classification accuracy while reducing training costs, we have introduced a novel defense algorithm based on model compression within the framework of Generative Adversarial Networks (GANs). This algorithm integrates GANs with model compression techniques, and the results of testing on public datasets have demonstrated an improved accuracy in defending against adversarial samples along with reduced training costs.

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Attention Standard Deviation Based Token Sparsification

Attention Standard Deviation Based Token Sparsification

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advanced Algorithms and Control Engineering (ICAACE), International Conference on

作者： En Gao Jinzhao We Guowu Yang University of Electronic Science and Technology of China Chengdu China Guilin University of Aerospace Technology Guilin China University of Electronic Science and Technology of China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu China

ISBN: (数字)9798350361445

ISBN: (纸本)9798350361452

Recently, model acceleration techniques have been applied to Vision Transformers (ViTs) to address their high computation costs, particularly in latency-sensitive domains. advanced techniques primarily focus on token sparsification, aiming to reduce the number of tokens in transformer blocks to accelerate inference. However, they often rely on trial-and-error pruning methods to find a balance between predictive accuracy and pruning efficiency, lacking quantitative characterization. To bridge this gap, this paper introduces an algorithm called Attention Standard deviation based Token Sparsification (ASTS). By utilizing the standard deviation of attention map weights as the threshold for pruning, ASTS removes a specific number of tokens with the lowest attention weights. This approach efficiently adjusts the trade-off between predictive accuracy and pruning efficiency, eliminating the need for extensive exploration in a vast hyper-parameter space. Through extensive experiments on various benchmarks, we empirically validate the effectiveness of the proposed pruning method.

关键词： Measurement Costs Control engineering Object detection Transformers Throughput Prediction algorithms

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Dangerous Action Recognition for Ship Sailing to Limited Resource Environment

Dangerous Action Recognition for Ship Sailing to Limited Res...

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

作者： Shengchao Yuan Yimin Zhou Lei Shi Yongxin Huang School of Cyberspace Security Chengdu University of Information Technology Chengdu China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu China

Action recognition is a comprehensive application prospect in the surveillance video source but most of the well-known models have significant computational cost feature. Take a bottom-up algorithm named OpenPose as an example, it consumes a lot of time to solve a task. If dangerous action monitoring is deployed on overseas ships, which can not afford such a large amount of computation and storage cost. Meanwhile, a oversea ship is mostly offline during ocean voyages and cannot upload and download data. This paper addresses this issue by improving on the PyTorch-OpenPose model, adjusting the resolution of the input images and removing some of the unneeded skeletal key-point information from the model. Experimental results demonstrate that the execution time is reduced by about 71%, while the memory footprint is reduced by about 62%.

关键词： Image resolution Computational modeling Oceans Surveillance Object detection Knowledge discovery Data mining

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Twitter Bots in Cyber-Physical-Social systems: Detection and Estimation Based on the SEIR Model

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security and Communication Networks 2023年第1期2023卷

作者： Zhang, Weisha Lu, Jiazhong Liu, Yulin Liu, Xiaojun School of Foreign Languages University of Electronic Science and Technology of China Chengdu611731 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province School of Cybersecurity Chengdu University of Information Technology Chengdu610225 China

Bots are now part of the social media landscape, and thus, a threat to cyber-physical-social systems (CPSSs). A better understanding of their characteristic behaviors and estimation of their impact on public opinion could help improve the algorithms to identify bots and help develop strategies to reduce their influence. The cosine function-based algorithm is able to compare the similarity between tweets and restore the course of information circulation. Combined with malicious features of an account, our method could effectively detect bots. We implement SEIR model to compute tweets with the hashtag #Huawei 5G and divide the trend propagation into the following four phases: formation, fermentation, explosion, and decay of trend. Sentiment analysis revealed the change of emotion and opinion among normal users in different stages and the manipulation attempt of bots behind it. Experiment results show that bots have very limited relation to users' stance in whole. In early phase bots could affect those who are neutral. The influence of bots declines in later stage. Polarized views can hardly be changed. © 2023 Weisha Zhang et al.

关键词： Sentiment analysis

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A Botnet Data Collection Method for Industrial Internet

A Botnet Data Collection Method for Industrial Internet

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

作者： Shu Jian Xian Bo Wang Chenli Lu Jiazhong Huang Yuanyuan Han Shuo School of Cybersecurity Chengdu University of Information Technology Chengdu Sichuan China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu Sichuan China Department of Statistics and Data Sciences Northwestern University Evanston IL USA

Industrial internet security is related to industrial production operations and national economic security. Network intrusion detection dataset in industrial control environment can effectively train detection models and mitigate network attacks, which is an indispensable component for industrial internet security. In this paper, we propose a data collection method for industrial Internet-oriented botnet by classifying traffic packets according to network traffic features and industrial control identification codes, containing the status of industrial control devices. This method can greatly improve the detection accuracy of the model in the industrial internet. To create a botnet incursion dataset in the industrial control environment, we simultaneously employed the existing industrial control devices to gather both industrial control and botnet network traffic. Finally, we used an AI-based model to detect the botnet.

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