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A Privacy Preservation Method for Attributed Social Network Based on Negative Representation of Information

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 1045-1075页

作者： Hao Jiang Yuerong Liao Dongdong Zhao Wenjian Luo Xingyi Zhang Key Laboratory of Intelligent Computing and Signal Processing of ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei230601China Chongqing Research institute School of Computer Science and Artificial IntelligenceWuhan University of TechnologyWuhan430070China Guangdong Provincial Key Laboratory of Novel security Intelligence Technologies School of Computer Science and TechnologyHarbin Institute of TechnologyShenzhen518055China

Due to the presence of a large amount of personal sensitive information in social networks,privacy preservation issues in social networks have attracted the attention of many *** by the self-nonself discrimination paradigmin the biological immune system,the negative representation of information indicates features such as simplicity and efficiency,which is very suitable for preserving social network ***,we suggest a method to preserve the topology privacy and node attribute privacy of attribute social networks,called ***,a negative survey-based method is developed to disturb the relationship between nodes in the social network so that the topology structure can be kept ***,a negative database-based method is proposed to hide node attributes,so that the privacy of node attributes can be preserved while supporting the similarity estimation between different node attributes,which is crucial to the analysis of social *** evaluate the performance of the AttNetNRI,empirical studies have been conducted on various attribute social networks and compared with several state-of-the-art methods tailored to preserve the privacy of social *** experimental results show the superiority of the developed method in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topology disturbing and attribute hiding *** experimental results show the superiority of the developed methods in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topological interference and attribute-hiding components.

关键词： Attributed social network topology privacy node attribute privacy negative representation of information negative survey negative database

MRm-DLDet:a memory-resident malware detection framework based on memory forensics and deep neural network

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Cybersecurity 2024年第1期7卷 88-109页

作者： Jiaxi Liu Yun Feng Xinyu Liu Jianjun Zhao Qixu Liu institute of Information Engineering Chinese Academy of SciencesBeijing 100085China School of Cyber security University of Chinese Academy of SciencesBeijing 100049China

Cyber attackers have constantly updated their attack techniques to evade antivirus software detection in recent *** popular evasion method is to execute malicious code and perform malicious actions only in ***-cious programs that use this attack method are called memory-resident malware,with excellent evasion capability,and have posed huge threats to cyber *** static and dynamic methods are not effective in detect-ing memory-resident *** addition,existing memory forensics detection solutions perform unsatisfactorily in detection rate and depend on massive expert knowledge in memory *** paper proposes MRm-DLDet,a state-of-the-art memory-resident malware detection framework,to overcome these ***-DLDet first builds a virtual machine environment and captures memory dumps,then creatively processes the memory dumps into RGB images using a pre-processing technique that combines deduplication and ultra-high resolution image cropping,followed by our neural network MRmNet in MRm-DLDet to fully extract high-dimensional features from memory dump files and detect *** receives the labeled sub-images of the cropped high-resolution RGB images as input of ResNet-18,which extracts the features of the *** trains a network of gated recurrent units with an attention ***,it determines whether a program is memory-resident malware based on the detection results of each sub-image through a specially designed voting *** created a high-quality dataset consisting of 2,060 benign and memory-resident *** other words,the dataset contains 1,287,500 labeled sub-images cut from the MRm-DLDet transformed ultra-high resolution RGB *** implement MRm-DLDet for Windows 10,and it performs better than the latest methods,with a detection accuracy of up to 98.34%.Moreover,we measured the effects of mimicry and adversarial attacks on MRm-DLDet,and the experimental results demonstrated the robustness

关键词： Memory-resident malware Memory forensics Malware detection Deep learning Ultra-high resolution image

A strategy to synthesize phosphorus-containing nickel phyllosilicate whiskers to enhance the flame retardancy of epoxy composites with excellent mechanical and dry-friction properties

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Frontiers of Chemical Science and Engineering 2024年第3期18卷 39-50页

作者： Shibin Nie Zongquan Zhao Yuxuan Xu Wei He Wenli Zhai Jinian Yang School of public security and Emergency Management Anhui University of Science and TechnologyHefei231131China School of Safety Science and Engineering Anhui University of Science and TechnologyHuainan232001China School of Materials Science and Engineering Anhui University of Science and TechnologyHuainan232001China institute of Energy Hefei Comprehensive National Science CenterHefei230000China

To enhance the fire safety and wear resistance of epoxy,phosphorus-containing nickel phyllosilicate whiskers(FP-NiPS)were synthesized using a facile hydrothermal technology,with 9,10-dihydro-9-oxa-10-phosphaphenanthrene as the organic *** impacts of FP-NiPS on the thermal stability,flame retardancy,and mechanical and tribological properties of EP composites were *** findings demonstrated that 5 wt%FP-NiPS elevated the limiting oxygen index of the EP composite from 23.8%to 28.4%,achieving a V-0 rating during vertical burning ***-NiPS could enhance the thermal stability of epoxy resin(EP)and facilitate the development of a dense and continuous carbon layer,thereby significantly improving the fire safety of the EP *** FP-NiPS led to an 8.2%increase in the tensile strength and a 38.8%increase in the elastic modulus of the EP composite,showing outstanding mechanical ***,FP-NiPS showed remarkable potential in enhancing the wear resistance of *** wear rate of 1 wt%FP-NiPS is 2.34×10^(−5) mm^(3)·N^(−1)·m^(−1),a decrease of 66.7%compared to *** work provides a novel promising modification method to enhance the fire safety,mechanical and wear resistance properties of EP.

关键词： nickel phyllosilicate epoxy resin flame retardant wear resistance mechanical property

Anomaly Detection in Imbalanced Encrypted Traffic with Few Packet Metadata-Based Feature Extraction

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Computer Modeling in Engineering & Sciences 2024年第10期141卷 585-607页

作者： Min-Gyu Kim Hwankuk Kim Department of Financial Information security Kookmin UniversitySeoul02707Republic of Korea Department of Information security Cryptography Mathematics Kookmin UniversitySeoul02707Republic of Korea

In the IoT(Internet of Things)domain,the increased use of encryption protocols such as SSL/TLS,VPN(Virtual Private Network),and Tor has led to a rise in attacks leveraging encrypted *** research on anomaly detection using AI(Artificial Intelligence)is actively progressing,the encrypted nature of the data poses challenges for labeling,resulting in data imbalance and biased feature extraction toward specific *** study proposes a reconstruction error-based anomaly detection method using an autoencoder(AE)that utilizes packet metadata excluding specific node *** proposed method omits biased packet metadata such as IP and Port and trains the detection model using only normal data,leveraging a small amount of packet *** makes it well-suited for direct application in IoT environments due to its low resource *** experiments comparing feature extraction methods for AE-based anomaly detection,we found that using flowbased features significantly improves accuracy,precision,F1 score,and AUC(Area Under the Receiver Operating Characteristic Curve)score compared to packet-based ***,for flow-based features,the proposed method showed a 30.17%increase in F1 score and improved false positive rates compared to Isolation Forest and ***,the proposedmethod demonstrated a 32.43%higherAUCwhen using packet features and a 111.39%higher AUC when using flow features,compared to previously proposed oversampling *** study highlights the impact of feature extraction methods on attack detection in imbalanced,encrypted traffic environments and emphasizes that the one-class method using AE is more effective for attack detection and reducing false positives compared to traditional oversampling methods.

关键词： One-class anomaly detection feature extraction auto-encoder encrypted traffic CICIoT2023

A Trust Evaluation Mechanism Based on Autoencoder Clustering Algorithm for Edge Device Access of IoT

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Computers, Materials & Continua 2024年第2期78卷 1881-1895页

作者： Xiao Feng Zheng Yuan Cyber security Academy Beijing University of Posts and TelecommunicationsBeijing100876China Department of Cyberspace security Beijing Electronic Science and Technology InstituteBeijing100070China State Grid Info-Telecom Great Power Science and Technology Co. Ltd.Beijing102211China

First,we propose a cross-domain authentication architecture based on trust evaluation mechanism,including registration,certificate issuance,and cross-domain authentication processes.A direct trust evaluation mechanism based on the time decay factor is proposed,taking into account the influence of historical interaction *** weight the time attenuation factor to each historical interaction record for updating and got the new historical record *** refer to the beta distribution to enhance the flexibility and adaptability of the direct trust assessment model to better capture time trends in the historical *** we propose an autoencoder-based trust clustering *** perform feature extraction based on *** leibler(KL)divergence is used to calculate the reconstruction *** constructing a convolutional autoencoder,we introduce convolutional neural networks to improve training efficiency and introduce sparse constraints into the hidden layer of the *** sparse penalty term in the loss function measures the difference through the KL *** clustering is performed based on the density based spatial clustering of applications with noise(DBSCAN)clustering *** the clustering process,edge nodes have a variety of trustworthy attribute *** assign different attribute weights according to the relative importance of each attribute in the clustering process,and a larger weight means that the attribute occupies a greater weight in the calculation of ***,we introduced adaptive weights to calculate comprehensive trust *** experiments prove that our trust evaluation mechanism has excellent reliability and accuracy.

关键词： Cross-domain authentication trust evaluation autoencoder

Efficient privacy-preserving federated learning under dishonest-majority setting

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Science China(Information Sciences) 2024年第5期67卷 323-324页

作者： Yinbin MIAO Da KUANG Xinghua LI Tao LENG Ximeng LIU Jianfeng MA School of Cyber Engineering Xidian University Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College institute of Information Engineering Chinese Academy of Sciences School of Cyber security University of Chinese Academy of Sciences Key Laboratory of Information security of Network Systems Fuzhou University

Federated learning(FL) is an emerging distributed learning paradigm that solves the problem of isolated data by jointly learning the global model through distributed ***, recent studies have shown that FL may not always guarantee sufficient privacy preservation, this is mainly because the model parameters(e.g., weights or gradients)may leak sensitive information to malicious adversaries.

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Novel Rifle Number Recognition Based on Improved YOLO in Military Environment

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Computers, Materials & Continua 2024年第1期78卷 249-263页

作者： Hyun Kwon Sanghyun Lee Department of Artificial Intelligence and Data Science Korea Military AcademySeoulKorea Graduate School of Information security Korea Advanced Institute of Science and TechnologyDaejeonKorea

Deep neural networks perform well in image recognition,object recognition,pattern analysis,and speech *** military applications,deep neural networks can detect equipment and recognize *** military equipment,it is necessary to detect and recognize rifle management,which is an important piece of equipment,using deep neural *** have been no previous studies on the detection of real rifle numbers using real rifle image *** this study,we propose a method for detecting and recognizing rifle numbers when rifle image data are *** proposed method was designed to improve the recognition rate of a specific dataset using data fusion and transfer *** the proposed method,real rifle images and existing digit images are fusedas trainingdata,andthe final layer is transferredto theYolov5 *** detectionand recognition performance of rifle numbers was improved and analyzed using rifle image and numerical *** used actual rifle image data(K-2 rifle)and numeric image datasets,as an experimental *** was used as the machine learning *** results show that the proposed method maintains 84.42% accuracy,73.54% precision,81.81% recall,and 77.46% F1-score in detecting and recognizing rifle *** proposed method is effective in detecting rifle numbers.

关键词： Machine learning deep neural network rifle number recognition detection

A Sharding Scheme Based on Graph Partitioning Algorithm for public Blockchain

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Computer Modeling in Engineering & Sciences 2024年第6期139卷 3311-3327页

作者： Shujiang Xu Ziye Wang Lianhai Wang Miodrag J.Mihaljevi′c Shuhui Zhang Wei Shao Qizheng Wang Key Laboratory of Computing Power Network and Information security Ministry of EducationShandong Computer Science CenterQilu University of Technology(Shandong Academy of Sciences)Jinan250014China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan250014China Mathematical institute The Serbian Academy of Sciences and ArtsBelgrade11000Serbia

Blockchain technology,with its attributes of decentralization,immutability,and traceability,has emerged as a powerful catalyst for enhancing traditional industries in terms of optimizing business ***,transaction performance and scalability has become the main challenges hindering the widespread adoption of *** to its inability to meet the demands of high-frequency trading,blockchain cannot be adopted in many *** improve the transaction capacity,researchers have proposed some on-chain scaling technologies,including lightning networks,directed acyclic graph technology,state channels,and shardingmechanisms,inwhich sharding emerges as a potential scaling ***,excessive cross-shard transactions and uneven shard workloads prevent the sharding mechanism from achieving the expected *** paper proposes a graphbased sharding scheme for public blockchain to efficiently balance the transaction *** crossshard transactions and evening-out workloads among shards,the scheme reduces transaction confirmation latency and enhances the transaction capacity of the ***,the scheme can achieve a high-frequency transaction as well as a better blockchain *** results show that the scheme effectively reduces the cross-shard transaction ratio to a range of 35%-56%and significantly decreases the transaction confirmation latency to 6 s in a blockchain with no more than 25 shards.

关键词： Blockchain sharding graph partitioning algorithm

Conditional selection with CNN augmented transformer for multimodal affective analysis

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 917-931页

作者： Jianwen Wang Shiping Wang Shunxin Xiao Renjie Lin Mianxiong Dong Wenzhong Guo College of Computer and Data Science Fuzhou UniversityFuzhouChina College of Computer and Cyber security Fujian Normal UniversityFuzhouChina Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhouChina Digital Fujian institute of Big Data security Technology Fujian Normal UniversityFuzhouChina Department of Sciences and Informatics Muroran Institute of TechnologyMuroranJapan

Attention mechanism has been a successful method for multimodal affective analysis in recent years. Despite the advances, several significant challenges remain in fusing language and its nonverbal context information. One is to generate sparse attention coefficients associated with acoustic and visual modalities, which helps locate critical emotional se-mantics. The other is fusing complementary cross‐modal representation to construct optimal salient feature combinations of multiple modalities. A Conditional Transformer Fusion Network is proposed to handle these problems. Firstly, the authors equip the transformer module with CNN layers to enhance the detection of subtle signal patterns in nonverbal sequences. Secondly, sentiment words are utilised as context conditions to guide the computation of cross‐modal attention. As a result, the located nonverbal fea-tures are not only salient but also complementary to sentiment words directly. Experi-mental results show that the authors’ method achieves state‐of‐the‐art performance on several multimodal affective analysis datasets.

关键词： affective computing data fusion information fusion multimodal approaches