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A New Encrypted Traffic Identification Model Based on VAE-LSTM-DRN

computers, Materials & Continua 2024年第1期78卷 569-588页

作者： Haizhen Wang Jinying Yan Na Jia College of Computer and Control Engineering Qiqihar UniversityQiqihar161006China Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar UniversityQiqiharChina

Encrypted traffic identification pertains to the precise acquisition and categorization of data from traffic datasets containing imbalanced and obscured *** extraction of encrypted traffic attributes and their subsequent identification presents a formidable *** existing models have predominantly relied on direct extraction of encrypted traffic data from imbalanced datasets,with the dataset’s imbalance significantly affecting the model’s *** the present study,a new model,referred to as UD-VLD(Unbalanced Dataset-VAE-LSTM-DRN),was proposed to address above *** proposed model is an encrypted traffic identification model for handling unbalanced *** encoder of the variational autoencoder(VAE)is combined with the decoder and Long-short term Memory(LSTM)in UD-VLD model to realize the data enhancement processing of the original unbalanced *** enhanced data is processed by transforming the deep residual network(DRN)to address neural network gradient-related ***,the data is classified and *** UD-VLD model integrates the related techniques of deep learning into the encrypted traffic recognition technique,thereby solving the processing problem for unbalanced *** UD-VLD model was tested using the publicly available Tor dataset and VPN *** UD-VLD model is evaluated against other comparative models in terms of accuracy,loss rate,precision,recall,F1-score,total time,and ROC *** results reveal that the UD-VLD model exhibits better performance in both binary and multi classification,being higher than other encrypted traffic recognition models that exist for unbalanced ***,the evaluation performance indicates that the UD-VLD model effectivelymitigates the impact of unbalanced data on traffic *** can serve as a novel solution for encrypted traffic identification.

关键词： Data enhancement LSTM deep residual network VAE

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FPIM: Fair and Privacy-Preserving Incentive Mechanism in Mobile Crowdsensing 24th

FPIM: Fair and Privacy-Preserving Incentive Mechanism in M...

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

作者： Lin, Ruonan Zhang, Yuanyuan Bi, Renwan Huang, Ruihong Xiong, Jinbo Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fujian Normal University Fuzhou350117 China

ISBN: (纸本)9789819615476

Mobile crowdsensing (MCS) enables a group of mobile participants to use their intelligent sensing devices to complete the sensing tasks proposed by the task requester. The sensing data users submit is an important factor affecting the effectiveness of sensing task results. How to fairly incentivize users while protecting their privacy is an urgent problem that needs to be solved. Distributing user rewards based on data quality is the mainstream method for achieving fair incentives. However, most current work only uses a subjective approach to weigh different factors in data quality evaluation. Therefore, this paper proposes a fair and privacy-preserving incentive mechanism (FPIM) scheme based on data quality evaluation in MCS. Specifically, we introduce the entropy weight method into the MCS task to achieve objective weighting in secure data quality evaluation protocol by designing new secure interval determination protocol (SIDP) and secure compare protocol (SCP). Experimental results show that the computational cost and communication overhead of the proposed protocols are reduced by about 33% and 25% when compared with the existing schemes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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Efficient Identity-Based Dynamic Cloud Storage Data Integrity Auditing with Incremental Updates for Handheld Mobile Devices 2nd

Efficient Identity-Based Dynamic Cloud Storage Data Integrit...

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2nd International Conference on Data security and Privacy Protection, DSPP 2024

作者： Huang, Yichi Zhang, Futai Yang, Wenjie Yang, Shaojun Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fujian Normal University Fuzhou350117 China

ISBN: (纸本)9789819785452

With rapid advancements in handheld mobile devices (HMDs), striking a balance between storage and data security is crucial. Cloud storage has attracted great attention of users for its convenience and availability. However, data integrity becomes challenging once data is uploaded to the cloud, as physical control is lost. In this paper, we propose an efficient identity-based dynamic cloud storage data integrity auditing protocol for HMDs. Our protocol simplifies certificate management and avoids the use of Map to Point (MtP) operation during private key extraction, resulting in a 50% improvement in private key verification efficiency compared to other protocols. We optimize tag generation computation, enhancing efficiency for computation-constrained users. Furthermore, our protocol supports incremental updates for dynamic data operations. We provide detailed system and security models, demonstrating the protocol’s security in ensuring data integrity and auditing soundness. Performance comparisons reveal the computation cost advantages of our protocol, making it ideal for application in cloud-based HMDs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data integrity

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A DDoS Identification Method for Unbalanced Data CVWGG

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computers, Materials & Continua 2024年第12期81卷 3825-3851页

作者： Haizhen Wang Na Jia Yang He Pan Tan College of Computer and Control Engineering Qiqihar UniversityQiqihar161006China Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar UniversityQiqihar161006China

As the popularity and dependence on the Internet increase,DDoS(distributed denial of service)attacks seriously threaten network *** accurately distinguishing between different types of DDoS attacks,targeted defense strategies can be formulated,significantly improving network protection *** attacks usually manifest as an abnormal increase in network traffic,and their diverse types of attacks,along with a severe data imbalance,make it difficult for traditional classification methods to effectively identify a small number of attack *** solve this problem,this paper proposes a DDoS recognition method CVWGG(Conditional Variational Autoencoder-Wasserstein Generative Adversarial network-gradient penalty-Gated Recurrent Unit)for unbalanced data,which generates less noisy data and high data quality compared with existing *** mainly includes unbalanced data processing for CVWG,feature extraction,and *** uses the CVAE(Conditional Variational Autoencoder)to improve the WGAN(Wasserstein Generative Adversarial network)and introduces a GP(gradient penalty)term to design the loss function to generate balanced data,which enhances the learning ability and stability of the ***,the GRU(Gated Recurrent Units)are used to capture the temporal features and patterns of the ***,the logsoftmax function is used to differentiate DDoS attack *** PyCharm and Python 3.10 for programming and evaluating performance with metrics such as accuracy and precision,the results show that the method achieved accuracy rates of 96.0%and 97.3%on two datasets,***,comparison and ablation experiment results demonstrate that CVWGG effectively mitigates the imbalance between DDoS attack categories,significantly improves the classification accuracy of different types of attacks and provides a valuable reference for network security defense.

关键词： Conditional variational autoencoder generating adversarial networks DDoS attack

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Recommendation System Based on Perceptron and Graph Convolution network

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computers, Materials & Continua 2024年第6期79卷 3939-3954页

作者： Zuozheng Lian Yongchao Yin Haizhen Wang College of Computer and Control Engineering Qiqihar UniversityQiqihar161006China Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar UniversityQiqihar161006China

The relationship between users and items,which cannot be recovered by traditional techniques,can be extracted by the recommendation algorithm based on the graph convolution *** current simple linear combination of these algorithms may not be sufficient to extract the complex structure of user interaction *** paper presents a new approach to address such issues,utilizing the graph convolution network to extract association *** proposed approach mainly includes three modules:Embedding layer,forward propagation layer,and score prediction *** embedding layer models users and items according to their interaction information and generates initial feature vectors as input for the forward propagation *** forward propagation layer designs two parallel graph convolution networks with self-connections,which extract higher-order association relevance from users and items separately by multi-layer graph ***,the forward propagation layer integrates the attention factor to assign different weights among the hop neighbors of the graph convolution network fusion,capturing more comprehensive association relevance between users and items as input for the score prediction *** score prediction layer introduces MLP(multi-layer perceptron)to conduct non-linear feature interaction between users and items,***,the prediction score of users to items is *** recall rate and normalized discounted cumulative gain were used as evaluation *** proposed approach effectively integrates higher-order information in user entries,and experimental analysis demonstrates its superiority over the existing algorithms.

关键词： Recommendation system graph convolution network attention mechanism multi-layer perceptron

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Achieving dynamic privacy measurement and protection based on reinforcement learning for mobile edge crowdsensing of IoT

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Digital Communications and networks 2024年第2期10卷 380-388页

作者： Renwan Bi Mingfeng Zhao Zuobin Ying Youliang Tian Jinbo Xiong Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber SecurityFujian Normal UniversityFuzhou350117China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin541004China Faculty of Data Science City University of Macao999078MacaoChina State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China

With the maturity and development of 5G field,Mobile Edge CrowdSensing(MECS),as an intelligent data collection paradigm,provides a broad prospect for various applications in ***,sensing users as data uploaders lack a balance between data benefits and privacy threats,leading to conservative data uploads and low revenue or excessive uploads and privacy *** solve this problem,a Dynamic Privacy Measurement and Protection(DPMP)framework is proposed based on differential privacy and reinforcement ***,a DPM model is designed to quantify the amount of data privacy,and a calculation method for personalized privacy threshold of different users is also ***,a Dynamic Private sensing data Selection(DPS)algorithm is proposed to help sensing users maximize data benefits within their privacy ***,theoretical analysis and ample experiment results show that DPMP framework is effective and efficient to achieve a balance between data benefits and sensing user privacy protection,in particular,the proposed DPMP framework has 63%and 23%higher training efficiency and data benefits,respectively,compared to the Monte Carlo algorithm.

关键词： Mobile edge crowdsensing Dynamic privacy measurement Personalized privacy threshold Privacy protection Reinforcement learning

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THL: Fast Targeted High Utility Itemset Mining within Length Consideration

Journal of Network Intelligence

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Journal of network Intelligence 2025年第1期10卷 541-558页

作者： Xu, Peiming Jiang, Yixin Kuang, Xiaoyun Luo, Yu Chen, Jiahui Guangdong Provincial Key Laboratory of Power System Network Security CSG Guangzhou510000 China School of Computer Science and Technology Guangdong University of Technology Guangzhou510006 China

High utility itemset mining is a research topic of the pattern mining domain, which has been widely applied in reality during the past decade. Although most high utility itemset mining algorithms show significant execution performance regarding runtime and memory usage, numerous results are useless for users. Targeted high utility itemset mining can reduce the number of high utility itemsets and improve the efficiency of pattern analysis. However, the targeted high utility itemset mining task aims to find targeted high utility itemsets with as many items as possible. Users are not that interested in long results in actual scenarios. Therefore, in this article, we first formulate the problem and then address this issue by proposing a new algorithm named THL for mining targeted high utility itemset within length constraints, based on utility list structure. Furthermore, to improve the performance of the novel algorithm, THL adopts several efficiency pruning strategies, including length and utility aspects. Finally, we conduct an extensive experimental evaluation to compare the SOTA algorithms with THL on several datasets. © 2025, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： data mining high utility itemset length constraint targeted items

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Fusion of Time-Frequency Features in Contrastive Learning for Shipboard Wind Speed Correction

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Journal of Ocean University of China 2025年第2期24卷 377-386页

作者： SONG Jian HUANG Meng LI Xiang ZHANG Zhenqiang WANG Chunxiao ZHAO Zhigang Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250000China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250000China

Accurate wind speed measurements on maritime vessels are crucial for weather forecasting,sea state prediction,and safe ***,vessel motion and challenging environmental conditions often affect measurement *** address this issue,this study proposes an innovative framework for correcting and predicting shipborne wind *** integrating a main network with a momentum updating network,the proposed framework effectively extracts features from the time and frequency domains,thereby allowing for precise adjustments and predictions of shipborne wind speed *** using real sensor data collected at the Qingdao Oceanographic Institute demonstrates that the proposed method outperforms existing approaches in single-and multi-step predictions compared to existing methods,achieving higher accuracy in wind speed *** proposed innovative approach offers a promising direction for future validation in more realistic maritime onboard scenarios.

关键词： time series prediction wind speed correction comparative learning shipborne sensor

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High-Capacity Image Hiding via Compressible Invertible Neural network 24th

High-Capacity Image Hiding via Compressible Invertible Neur...

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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 College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China Hebei Key Laboratory of Network and Information Security Shijiazhuang050024 China

ISBN: (纸本)9789819615278

Image steganography involves the concealment of confidential information within images, rendering it undetectable to unauthorized observers, and subsequently retrieving the hidden information following secure transmission. Numerous investigations into image steganography employ invertible neural networks. Nevertheless, the intricate architecture of these models poses significant challenges to both their training and inference processes. Consequently, this paper proposes a high-capacity image steganography scheme utilizing compressible modules. We propose a multi-scale attention module that compresses the model structure through structural reparameterization post-training, thereby enhancing inference speed. Additionally, we employ a pre-trained image autoencoder to extract deep features from large-scale images, facilitating high-capacity steganography within invertible neural networks by concealing key features. Experimental results indicate that our approach offers superior image quality and model inference speed, while significantly enhancing security relative to existing methodologies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image enhancement

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Probabilistic Reliability via Subsystem Structures of Arrangement Graph networks

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IEEE Transactions on Reliability 2024年第1期73卷 279-289页

作者： Huang, Yanze Lin, Limei Xu, Li Hsieh, Sun-Yuan Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fuzhou350118 China Fujian Normal University College of Computer and Cyber Security Key Laboratory of Network Security and Cryptology Fuzhou350117 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan

With the rapid growth of the number of processors in a multiprocessor system, faulty processors occur in it with a probability that rises quickly. The probability of a subsystem with an appropriate size being fault-free in a definite time interval is a significant and practical measure of the reliability for a multiprocessor system, which characterizes the functionality of a multiprocessor system well. Motivated by the study of subgraph reliability, as well as the attractive structure and fault tolerance properties of (n, k)-arrangement graph A n, k, we focus on the subgraph reliability for A n, k under the probabilistic fault model in this article. First, we investigate intersections of no more than four subgraphs in A n, k, and classify all the intersecting modes. Second, we focus on the probability P(q, A n, k n-1, k-1) with which at least one (n-1, k-1)-subarrangement graph is fault-free in A n, k, when given a uniform probability q with which a single vertex is fault-free, and we establish the P(q, A n, kn-1, k-1) by adopting the principle of inclusion-exclusion under the probabilistic fault model. Finally, we study the probabilistic fault model involving a nonuniform probability with which a single vertex is fault-free, and we prove that the P(q, A n, kn-1, k-1) under both models is very close to the asymptotic value by both theoretical arguments and experimental results. © 1963-2012 IEEE.

关键词： Fault tolerance

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