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7th International Conference on Attacks and Defenses for Internet-of-Things, ADIoT 2024

作者： Yu, Xiaomin Yu, Xiaokun College of Computer and Control Engineering Qiqihar University Qiqihar China Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar University Qiqihar China Heilongjiang Jiaotong Polytechnic Qiqihar China

ISBN: (纸本)9783031855924

Recently, with the rapid development of communication technology, location-based services (LBS) in indoor environments have penetrated into various aspects of people’s daily lives. Wireless local area networks (WLAN) have become the preferred choice for location-based services due to their broad deployment and low cost. However, the typical non-line-of-sight characteristics of indoor environments result in severe fluctuations in received signal strength (RSS), signal coverage vulnerabilities can exacerbate environmental interference, making positioning results more vulnerable to attacks or tampering. The layout of indoor network elements is a key factor that constrains signal coverage rate. This paper focuses on the optimization of indoor network element layout, proposes an indoor network element layout optimization model based on signal coverage and quality in indoor environments and introduces an Adaptive Simulation Annealing Non-inertial Opposite Particle Swarm Optimization algorithm (ASA-NRPSO). Firstly, a reverse strategy is introduced into the particle flight process of the Particle Swarm Optimization (PSO) algorithm, and high-quality particles are selected for the next generation iteration. In the selection of the reverse strategy, the simulated annealing idea is integrated to adaptively select the reverse strategy based on the needs of the particles at different stages, which avoids the particles falling into local optima. The inertia term in the particle swarm velocity update formula is replaced with group information for non-inertial updates. This can more fully utilize the global information of the population to guide the movement of the next generation of particles and improve the convergence speed of the particle swarm. The elite mutation strategy is used to increase the diversity of particles and improve the global search capability of the algorithm. This method ensures the rapid generation of network element layout to improve the signal coverage rate

关键词： Location based services

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PyionNet: Pyramid Progressive Cross-Fusion network for Joint Classification of Hyperspectral and LiDAR data

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12042-12058页

作者： Pan, Haizhu Zhang, Quanxiu Ge, Haimiao Liu, Moqi Shi, Cuiping Qiqihar University College of Computer and Control Engineering Qiqihar161000 China Qiqihar University Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar161000 China Xi'an Jiaotong University School of Information and Communications Engineering Shanxi Xi'an710049 China Huzhou University College of Information Engineering Huzhou313000 China

Joint hyperspectral (HS) and Light detection and Ranging (LiDAR) data for land cover classification have received widespread attention. Nevertheless, existing methods often struggle with multi-scale feature extraction and multi-source feature fusion. To address these issues, this paper proposes a pyramid progressive cross-fusion network, named PyionNet, for joint classification of HS and LiDAR data. Technically, the proposed PyionNet incorporates a pyramid multi-scale extraction (PME) module cascaded with a progressive cross-fusion (PCF) module, which are designed to extract multi-scale features and fuse multi-source features, respectively. Specifically, the PME module employs pyramid spectral and spatial convolutional blocks to extract and aggregate features from HS and LiDAR data, respectively, then uses an expansion-compression strategy to eliminate redundant information, and thus achieve a compact multi-scale feature representation. Moreover, the PCF module utilizes the transformer architecture and introduces two innovative feature fusion blocks, i.e., the discrepancy feature fusion (DFF) block and the common feature fusion (CFF) block, to progressively integrate multi-source and multi-scale features extracted by the PME module. The DFF block is designed to integrate discrepancy features between HS and LiDAR data to enhance complementary information. Subsequently, the CFF block focuses on fusing common features to progressively enrich the fused representation of the DFF block and further enhance the representation power. Experiments on three real-world datasets demonstrate that the proposed PyionNet outperforms state-of-the-art methods in both qualitative and quantitative results. © 2008-2012 IEEE.

关键词： Feature Selection

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A novel quantum multiparty summation protocol based on a cooperative random number mechanism

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EPJ Quantum Technology 2025年第1期12卷 1-19页

作者： Zhang, Kejia Zhang, Yu Zhang, Xue Liu, Hongyan Song, Tingting Du, Gang School of Computer Science and Big Data (School of Cybersecurity) Heilongjiang University Harbin China State Key Laboratory of Public Big Data Guizhou University Guiyang China School of Mathematical Science Heilongjiang University Harbin China Institute for Cryptology and Network Security Heilongjiang University Harbin China School of China Construction Bank University Northeastern Institute Harbin China College of Information Science and Technology Jinan University Guangzhou China

As a particular area of quantum security multiparty computation, quantum secure multiparty summation plays a critical role in modern cryptography. It is widely known that most of the existing quantum summation protocols are based on an honest or semi-honest third party (TP). However, the introduced TP makes the protocol difficult to implement in practice, as it may face a single-point-of-failure attack on TP. Although some TP-free protocols are proposed to mitigate this risk, the increased cost of communication reduces its efficiency. To address these issues, a novel quantum-secure multiparty summation protocol based on a cooperative random number distribution mechanism (QMS-CRM) is proposed in this paper for the first time. During it, this mechanism is designed using Shamir’s secret sharing scheme. Furthermore, this approach eliminates the requirement for random number exchange between participants without the help of TP, enhancing the efficiency of the protocol. The security analysis demonstrates that the proposed protocol can resist both external attacks and collusion attacks by up to $n - 2$ participants. Finally, we simulated the protocol on the IBM Quantum Cloud platform, confirming its feasibility.

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Predicting metabolite-disease associations based on dynamic adaptive feature learning architecture

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Computer Methods and Programs in Biomedicine 2025年 268卷 108867-108867页

作者： Wang, Bo Liu, Shiyu Du, Xiaoxin Zang, Jianfei Zhang, Chunyu Yang, Xue He, Yang School of Computer and Control Engineering Qiqihar University Qiqihar 161006 PR China Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar University Qiqihar 161006 PR China

AbstractBackground and ObjectiveIn recent years, the association between metabolites and complex human diseases has increasingly been recognized as a major research focus. Traditional wet-lab experiments are considered time-consuming and labor-intensive, while computational methods have been shown to significantly enhance research efficiency. However, existing methods for predicting metabolite-disease associations primarily depend on predefined similarity metrics and static network structures, often failing to capture the complex interactions among node neighborhoods within metabolite and disease networks. This limitation hinders the capture of deeper dynamic relationships between metabolites and diseases, resulting in information loss and noise that deteriorate prediction performance. MethodsAn innovative dynamic adaptive feature learning architecture (DAF-LA) is proposed to predict metabolite-disease associations. This architecture integrates dynamic subgraph construction and an adaptive feature enhancement mechanism, enabling high-precision feature learning and association prediction through progressive optimization from initial to high-order feature representations. ResultsThe architecture was evaluated through five-fold cross-validation, achieving an AUC of 0.9742 and an AUPR of 0.9734. Additionally, the case study demonstrates that DAF-LA accurately predicts metabolites associated with Alzheimer's disease, Type 2 diabetes mellitus and Parkinson's disease. ConclusionsThe results demonstrate that our method effectively uncovers potential associations between metabolites and diseases through dynamic topological modeling and multi-scale collaborative learning. It enables faster identification of likely metabolite-disease relationships, reduces the time and resource costs associated with inefficient large-scale screening in traditional wet-lab experiments, and provides more targeted guidance for subsequent biological validation.

关键词： Metabolites Disease Dynamic subgraph Adaptive autoencoder CNN

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

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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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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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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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Multi-scale hierarchical cross fusion network for hyperspectral image and LiDAR classification

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Journal of the Franklin Institute 2025年第9期362卷

作者： Pan, Haizhu Li, Xuehu Ge, Haimiao Wang, Liguo Yu, Xiaomin College of Computer and Control Engineering Qiqihar University Qiqihar161000 China Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar University Qiqihar161000 China College of Information and Communication Engineering Dalian Nationalities University Dalian116000 China

As the satellite sensor technology continues to advance, acquiring hyperspectral images (HSI) and Light detection and Ranging (LiDAR) data have become increasingly accessible. Consequently, the fusion of these two types of data has become very popular in land cover classification tasks. However, there are still some deficiencies in the current methods, such as insufficient extraction of distinctive features of multi-source data, and inadequate fusion caused by poor use of the complementarity and cooperation in multi-source data. Therefore, we propose a multi-scale hierarchical cross fusion network (MSHCFNet) for the fusion classification of HSI and LiDAR. It includes four parts. Firstly, the multi-scale cascading feature extraction module is used to extract fully multi-scale spatial features. Secondly, the hierarchical fusion module is used to integrate spatial features by cross fusion and group fusion. Thirdly, the spectral signature enhancement module is used to enhance the spectral signature, so that the discriminant information in the spectral signature can be used effectively. Finally, the decision fusion module is used to classify the types of ground objects. A large number of experiments on three publicly available datasets show that the proposed method has the most advanced classification efficacy compared with existing methods. © 2025

关键词： Feature Selection

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Maximizing Influence in Temporal Social networks:A Node Feature-Aware Voting Algorithm

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Computers, Materials & Continua 2023年第12期77卷 3095-3117页

作者： Wenlong Zhu Yu Miao Shuangshuang Yang Zuozheng Lian Lianhe Cui College of Computer and Control Engineering Qiqihar UniversityQiqihar161006China Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar UniversityQiqihar161006China College of Teacher Education Qiqihar UniversityQiqihar161006China

Influence Maximization(IM)aims to select a seed set of size k in a social network so that information can be spread most widely under a specific information propagation model through this set of ***,most existing studies on the IM problem focus on static social network features,while neglecting the features of temporal social *** bridge this gap,we focus on node features reflected by their historical interaction behavior in temporal social networks,i.e.,interaction attributes and self-similarity,and incorporate them into the influence maximization algorithm and information propagation ***,we propose a node feature-aware voting algorithm,called ISVoteRank,for seed nodes ***,before voting,the algorithm sets the initial voting ability of nodes in a personalized manner by combining their *** the voting process,voting weights are set based on the interaction strength between nodes,allowing nodes to vote at different extents and subsequently weakening their voting ability *** process concludes by selecting the top k nodes with the highest voting scores as seeds,avoiding the inefficiency of iterative seed selection in traditional voting-based ***,we extend the Independent Cascade(IC)model and propose the Dynamic Independent Cascade(DIC)model,which aims to capture the dynamic features in the information propagation process by combining node ***,experiments demonstrate that the ISVoteRank algorithm has been improved in both effectiveness and efficiency compared to baseline methods,and the influence spread through the DIC model is improved compared to the IC model.

关键词： Temporal social networks influence maximization voting strategy interactive properties self-similarity

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A Positive Influence Maximization Algorithm in Signed Social networks

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Computers, Materials & Continua 2023年第8期76卷 1977-1994页

作者： Wenlong Zhu Yang Huang Shuangshuang Yang Yu Miao Chongyuan Peng College of Computer and Control Engineering Qiqihar UniversityQiqihar161006China Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar UniversityQiqihar161006China College of Teacher Education Qiqihar UniversityQiqihar161006China

The influence maximization(IM)problem aims to find a set of seed nodes that maximizes the spread of their influence in a social *** positive influence maximization(PIM)problem is an extension of the IM problem,which consider the polar relation of nodes in signed social networks so that the positive influence of seeds can be the most widely *** solve the PIM problem,this paper proposes the polar and decay related independent cascade(IC-PD)model to simulate the influence propagation of nodes and the decay of information during the influence propagation in signed social *** overcome the low efficiency of the greedy based algorithm,this paper defines the polar reverse reachable(PRR)set and devises a signed reverse influence sampling(SRIS)*** algorithm utilizes the ICPD model as well as the PRR set to select *** are two phases in *** is the sampling phase,which utilizes the IC-PD model to generate the PRR set and a binary search algorithm to calculate the number of needed PRR *** other is the node selection phase,which uses a greedy coverage algorithm to select optimal ***,Experiments on three real-world polar social network datasets demonstrate that SRIS outperforms the baseline algorithms in *** on the Slashdot dataset,SRIS achieves 24.7% higher performance than the best-performing compared algorithm under the weighted cascade model when the seed set size is 25.

关键词： Positive influencemaximization polar relation information decay reverse influence sampling

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