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BSTFNet:An Encrypted Malicious Traffic Classification Method Integrating Global Semantic and Spatiotemporal Features

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computers, Materials & Continua 2024年第3期78卷 3929-3951页

作者： Hong Huang Xingxing Zhang Ye Lu Ze Li Shaohua Zhou School of computer science and engineering Sichuan University of Science&EngineeringYibin644002China School of Mathematics and Statistics Sichuan University of Science&EngineeringYibin644002China

While encryption technology safeguards the security of network communications,malicious traffic also uses encryption protocols to obscure its malicious behavior.To address the issues of traditional machine learning methods relying on expert experience and the insufficient representation capabilities of existing deep learning methods for encrypted malicious traffic,we propose an encrypted malicious traffic classification method that integrates global semantic features with local spatiotemporal features,called BERT-based Spatio-Temporal Features Network(BSTFNet).At the packet-level granularity,the model captures the global semantic features of packets through the attention mechanism of the Bidirectional Encoder Representations from Transformers(BERT)model.At the byte-level granularity,we initially employ the Bidirectional Gated Recurrent Unit(BiGRU)model to extract temporal features from bytes,followed by the utilization of the Text Convolutional Neural Network(TextCNN)model with multi-sized convolution kernels to extract local multi-receptive field spatial features.The fusion of features from both granularities serves as the ultimate multidimensional representation of malicious traffic.Our approach achieves accuracy and F1-score of 99.39%and 99.40%,respectively,on the publicly available USTC-TFC2016 dataset,and effectively reduces sample confusion within the Neris and Virut categories.The experimental results demonstrate that our method has outstanding representation and classification capabilities for encrypted malicious traffic.

关键词： Encrypted malicious traffic classification bidirectional encoder representations from transformers text convolutional neural network bidirectional gated recurrent unit

Traffic engineering and Quality of Service in Hybrid Software Defined Networks

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China Communications 2024年第2期21卷 96-121页

作者： Samiullah Mehraban Rajesh Kumar Yadav IOT Lab Department of Computer Science&EngineeringDelhi Technological UniversityDelhi 110042India

For Future networks, many research projects have proposed different architectures around the globe;Software Defined Network(SDN) architectures, through separating Data and Control Layers, offer a crucial structure for it. With a worldwide view and centralized Control, the SDN network provides flexible and reliable network management that improves network throughput and increases link utilization. In addition, it supports an innovative flow scheduling system to help advance Traffic engineering(TE). For Medium and large-scale networks migrating directly from a legacy network to an SDN Network seems more complicated & even impossible, as there are High potential challenges, including technical, financial, security, shortage of standards, and quality of service degradation challenges. These challenges cause the birth and pave the ground for Hybrid SDN networks, where SDN devices coexist with traditional network devices. This study explores a Hybrid SDN network’s Traffic engineering and Quality of Services Issues. Quality of service is described by network characteristics such as latency, jitter, loss, bandwidth,and network link utilization, using industry standards and mechanisms in a Hybrid SDN Network. We have organized the related studies in a way that the Quality of Service may gain the most benefit from the concept of Hybrid SDN networks using different algorithms and mechanisms: Deep Reinforcement Learning(DRL), Heuristic algorithm, K path partition algorithm, Genetic algorithm, SOTE algorithm, ROAR method, and Routing Optimization with different optimization mechanisms that help to ensure high-quality performance in a Hybrid SDN Network.

关键词： DRL hSDN QoE QoS SDN TE

Japanese Sign Language Recognition by Combining Joint Skeleton-Based Handcrafted and Pixel-Based Deep Learning Features with Machine Learning Classification

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computer Modeling in engineering & sciences 2024年第6期139卷 2605-2625页

作者： Jungpil Shin Md.Al Mehedi Hasan Abu Saleh Musa Miah Kota Suzuki Koki Hirooka School of computer science and engineering The University of AizuAizuwakamatsu965-8580Japan department of computer science&engineering Rajshahi University of Engineering&TechnologyRajshahi6204Bangladesh

Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing communities.In Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japanese Sign Language(JSL)for communication.However,existing JSL recognition systems have faced significant performance limitations due to inherent complexities.In response to these challenges,we present a novel JSL recognition system that employs a strategic fusion approach,combining joint skeleton-based handcrafted features and pixel-based deep learning features.Our system incorporates two distinct streams:the first stream extracts crucial handcrafted features,emphasizing the capture of hand and body movements within JSL gestures.Simultaneously,a deep learning-based transfer learning stream captures hierarchical representations of JSL gestures in the second stream.Then,we concatenated the critical information of the first stream and the hierarchy of the second stream features to produce the multiple levels of the fusion features,aiming to create a comprehensive representation of the JSL gestures.After reducing the dimensionality of the feature,a feature selection approach and a kernel-based support vector machine(SVM)were used for the classification.To assess the effectiveness of our approach,we conducted extensive experiments on our Lab JSL dataset and a publicly available Arabic sign language(ArSL)dataset.Our results unequivocally demonstrate that our fusion approach significantly enhances JSL recognition accuracy and robustness compared to individual feature sets or traditional recognition methods.

关键词： Japanese Sign Language(JSL) hand gesture recognition geometric feature distance feature angle feature GoogleNet

Enhancing Multicriteria-Based Recommendations by Alleviating Scalability and Sparsity Issues Using Collaborative Denoising Autoencoder

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computers, Materials & Continua 2024年第2期78卷 2269-2286页

作者： S.Abinaya K.Uttej Kumar School of computer science&engineering Vellore Institute of TechnologyChennaiTamilnadu600127India

A Recommender System(RS)is a crucial part of several firms,particularly those involved in e-commerce.In conventional RS,a user may only offer a single rating for an item-that is insufficient to perceive consumer preferences.Nowadays,businesses in industries like e-learning and tourism enable customers to rate a product using a variety of factors to comprehend customers’preferences.On the other hand,the collaborative filtering(CF)algorithm utilizing AutoEncoder(AE)is seen to be effective in identifying user-interested items.However,the cost of these computations increases nonlinearly as the number of items and users increases.To triumph over the issues,a novel expanded stacked autoencoder(ESAE)with Kernel Fuzzy C-Means Clustering(KFCM)technique is proposed with two phases.In the first phase of offline,the sparse multicriteria rating matrix is smoothened to a complete matrix by predicting the users’intact rating by the ESAE approach and users are clustered using the KFCM approach.In the next phase of online,the top-N recommendation prediction is made by the ESAE approach involving only the most similar user from multiple clusters.Hence the ESAE_KFCM model upgrades the prediction accuracy of 98.2%in Top-N recommendation with a minimized recommendation generation time.An experimental check on the Yahoo!Movies(YM)movie dataset and TripAdvisor(TA)travel dataset confirmed that the ESAE_KFCM model constantly outperforms conventional RS algorithms on a variety of assessment measures.

关键词： Recommender systems multicriteria rating collaborative filtering sparsity issue scalability issue stacked-autoencoder Kernel Fuzzy C-Means Clustering

Dynamic Hand Gesture-Based Person Identification Using Leap Motion and Machine Learning Approaches

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computers, Materials & Continua 2024年第4期79卷 1205-1222页

作者： Jungpil Shin Md.AlMehedi Hasan Md.Maniruzzaman Taiki Watanabe Issei Jozume School of computer science and engineering The University of AizuAizuwakamatsuFukushima965-8580Japan department of computer science&engineering Rajshahi University of Engineering&TechnologyRajshahi6204Bangladesh

Person identification is one of the most vital tasks for network security. People are more concerned about theirsecurity due to traditional passwords becoming weaker or leaking in various attacks. In recent decades, fingerprintsand faces have been widely used for person identification, which has the risk of information leakage as a resultof reproducing fingers or faces by taking a snapshot. Recently, people have focused on creating an identifiablepattern, which will not be reproducible falsely by capturing psychological and behavioral information of a personusing vision and sensor-based techniques. In existing studies, most of the researchers used very complex patternsin this direction, which need special training and attention to remember the patterns and failed to capturethe psychological and behavioral information of a person properly. To overcome these problems, this researchdevised a novel dynamic hand gesture-based person identification system using a Leap Motion sensor. Thisstudy developed two hand gesture-based pattern datasets for performing the experiments, which contained morethan 500 samples, collected from 25 subjects. Various static and dynamic features were extracted from the handgeometry. Randomforest was used to measure feature importance using the Gini Index. Finally, the support vectormachinewas implemented for person identification and evaluate its performance using identification accuracy. Theexperimental results showed that the proposed system produced an identification accuracy of 99.8% for arbitraryhand gesture-based patterns and 99.6% for the same dynamic hand gesture-based patterns. This result indicatedthat the proposed system can be used for person identification in the field of security.

关键词： Person identification leap motion hand gesture random forest support vector machine

Spherical Bi_(2)O_(3)/ATO catalyst with N_(2)pre-reduction electrocatalytic reduction of CO_(2)to formic acid

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Journal of Environmental sciences 2024年第6期140卷 331-340页

作者： Junying Yi Yuli Chen Dongze Lai Bihong Lv Xiaomin Wu Guohua Jing department of Environmental science&engineering College of Chemical EngineeringHuaqiao UniversityXiamenFujian 361021China

To achieve carbon neutrality,the Chinese government needs to gain a comprehensive understanding of the sources and drivers of greenhouse gas(GHG)emissions,particularly at the county level.Anji County in eastern China is a typical example of an industrial transformation from quarrying to a low-carbon economy.This study analyzed the decoupling types and structural characteristics of GHG emissions and the driving factors of carbon dioxide(CO_(2))emissions in the Anji from 2006 to 2019,and explored the differences between countylevel and provincial-level or city-level results.It was observed that energy-related activities are the main source of GHG emissions in Anji and that economic development is the driving factor behind the increasing CO_(2)emissions.However,industrial transformation and upgradation coupled with the alternative use of clean energy limit the growth of GHG emissions.This study details the GHG emissions of county during the industrial transformation stage and provides corresponding policy recommendations for county governments.

关键词： N_(2)prereduction CO_(2)electroreduction Bi_(2)O_(3)/ATO catalyst Formate

A hierarchical clustering approach for colorectal cancer molecular subtypes identification from gene expression data

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Intelligent Medicine 2024年第1期4卷 43-51页

作者： Shivangi Raghav Aastha Suri Deepika Kumar Aakansha Aakansha Muskan Rathore Sudipta Roy department of computer science&engineering Bharati Vidyapeeth’s College of EngineeringNew DelhiIndia Artificial Intelligence&Data science Jio InstituteNavi Mumbai410206India

Background Colorectal cancer(CRC)is the second leading cause of cancer fatalities and the third most common human disease.Identifying molecular subgroups of CRC and treating patients accordingly could result in better therapeutic success compared with treating all CRC patients similarly.Studies have highlighted the significance of CRC as a major cause of mortality worldwide and the potential benefits of identifying molecular subtypes to tailor treatment strategies and improve patient outcomes.Methods This study proposed an unsupervised learning approach using hierarchical clustering and feature selection to identify molecular subtypes and compares its performance with that of conventional methods.The proposed model contained gene expression data from CRC patients obtained from Kaggle and used dimension reduction techniques followed by Z-score-based outlier removal.Agglomerative hierarchy clustering was used to identify molecular subtypes,with a P-value-based approach for feature selection.The performance of the model was evaluated using various classifiers including multilayer perceptron(MLP).Results The proposed methodology outperformed conventional methods,with the MLP classifier achieving the highest accuracy of 89%after feature selection.The model successfully identified molecular subtypes of CRC and differentiated between different subtypes based on their gene expression profiles.Conclusion This method could aid in developing tailored therapeutic strategies for CRC patients,although there is a need for further validation and evaluation of its clinical significance.

关键词： Machine learning Colorectal cancer Feature selection Classification Clustering

Surface-modified Ag@Ru-P25 for photocatalytic CO_(2) conversion with high selectivity over CH_(4) formation at the solid–gas interface

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Carbon Energy 2024年第1期6卷 182-196页

作者： Chaitanya B.Hiragond Sohag Biswas Niket SPowar Junho Lee Eunhee Gong Hwapyong Kim Hong Soo Kim Jin-Woo Jung Chang-Hee Cho Bryan M.Wong Su-Il In department of Energy science&engineering Daegu Gyeongbuk Institute of Science and Technology(DGIST)DaeguRepublic of Korea department of Chemical&Environmental engineering Materials Science&Engineering ProgramDepartment of ChemistryDepartment of Physics&AstronomyUniversity of California-RiversideRiversideCaliforniaUSA department of Physics and Chemistry Daegu Gyeongbuk Institute of Science and Technology(DGIST)DaeguRepublic of Korea Nicescience Corporation DaeguRepublic of Korea

Systematic optimization of the photocatalyst and investigation of the role of each component is important to maximizing catalytic activity and comprehending the photocatalytic conversion of CO_(2) reduction to solar fuels.A surface-modified Ag@Ru-P25 photocatalyst with H_(2)O_(2) treatment was designed in this study to convert CO_(2) and H_(2)O vapor into highly selective CH4.Ru doping followed by Ag nanoparticles(NPs)cocatalyst deposition on P25(TiO_(2))enhances visible light absorption and charge separation,whereas H_(2)O_(2) treatment modifies the surface of the photocatalyst with hydroxyl(–OH)groups and promotes CO_(2) adsorption.High-resonance transmission electron microscopy,X-ray photoelectron spectroscopy,X-ray absorption near-edge structure,and extended X-ray absorption fine structure techniques were used to analyze the surface and chemical composition of the photocatalyst,while thermogravimetric analysis,CO_(2) adsorption isotherm,and temperature programmed desorption study were performed to examine the significance of H_(2)O_(2) treatment in increasing CO_(2) reduction activity.The optimized Ag1.0@Ru1.0-P25 photocatalyst performed excellent CO_(2) reduction activity into CO,CH4,and C2H6 with a~95%selectivity of CH4,where the activity was~135 times higher than that of pristine TiO_(2)(P25).For the first time,this work explored the effect of H_(2)O_(2) treatment on the photocatalyst that dramatically increases CO_(2) reduction activity.

关键词： gas-phase CO_(2) reduction H_(2)O_(2) treatment plasmonic nanoparticles solar fuel photocatalyst surface modification

Room and cryogenic deformation behavior of AZ61 and AZ61-xCaO(x = 0.5, 1 wt.%) alloy

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Journal of Magnesium and Alloys 2024年第5期12卷 1996-2009页

作者： Umer Masood Chaudry Hafiz Muhammad Rehan Tariq Nooruddin Ansari Soo Yeol Lee Tea-Sung Jun department of Mechanical engineering Incheon National University Research Institute for engineering and Technology Incheon National University department of Materials science&engineering Chungnam National University

This study investigates the influence of CaO(0.5, 1(wt.%)) alloying on the microstructural evolution, texture development and deformation behavior of AZ61 magnesium alloy. The uniaxial tension tests at room(RT) and cryogenic(CT,-150 ℃) temperature were performed to investigate the twinability and dislocation behavior and its consequent effect on flow stress, ductility and strain hardening rate. The results showed that the AZ61-1CaO exhibited superior strength/ductility synergy at RT with a yield strength(YS) of 223 MPa and a ductility of 23% as compared to AZ61(178 MPa, 18.5%) and AZ61-0.5CaO(198 MPa, 21%). Similar trend was witnessed for all the samples during CT deformation, where increase in the YS and decrease in ductility were observed. The Mtex tools based in-grain misorientation axis(IGMA) analysis of RT deformed samples revealed the higher activities of prismaticslip in AZ61-CaO, which led to superior ductility.Moreover, subsequent EBSD analysis of CT deformed samples showed the increased fraction of fine {10-12} tension twins and nucleation of multiple {10-12} twin variants caused by higher local stress concentration at the grain boundaries, which imposed the strengthening by twin-twin interaction. Lastly, the detailed investigations on strengthening contributors showed that the dislocation strengthening has the highest contribution towards strength in all samples.

关键词： Magnesium Twinning Twinning variant EBSD Cryogenic deformation