Smart card based user authentication offers security and convenience in the remote user authentication system. On the other hand, a strong and secure scheme is needed to provide user authentication based on a smart card. Several schemes have been proposed, one of them is Lee's scheme proposed in 2015 [1]. However, based on Jung et al. analysis 2015[2], Lee's scheme has several weaknesses against impersonation and off-line password guessing attack. Moreover, the scheme is also failed to preserve user anonymity. This research proposed an improvement scheme to strengthen Lee's scheme [1]. For strengthening Lee's scheme, the proposed scheme uses Zero Knowledge and keyed hash function. The proposed scheme introduces an additional phase for generating a session key for securing the communication between two parties each other. Based on the experiment, it can be concluded that the proposed scheme is stronger than the previous scheme because the probability for breaking the proposed scheme using the off-line password guessing and impersonation attack is less than probability for breaking Lee's scheme.
{"title":"Strengthening the Security and Preserving User Anonymity of Remote User Authentication Scheme Using Smart Card","authors":"Abdullah Hanifan, Ari Moesriami Barmawi","doi":"10.1145/3478301.3478302","DOIUrl":"https://doi.org/10.1145/3478301.3478302","url":null,"abstract":"Smart card based user authentication offers security and convenience in the remote user authentication system. On the other hand, a strong and secure scheme is needed to provide user authentication based on a smart card. Several schemes have been proposed, one of them is Lee's scheme proposed in 2015 [1]. However, based on Jung et al. analysis 2015[2], Lee's scheme has several weaknesses against impersonation and off-line password guessing attack. Moreover, the scheme is also failed to preserve user anonymity. This research proposed an improvement scheme to strengthen Lee's scheme [1]. For strengthening Lee's scheme, the proposed scheme uses Zero Knowledge and keyed hash function. The proposed scheme introduces an additional phase for generating a session key for securing the communication between two parties each other. Based on the experiment, it can be concluded that the proposed scheme is stronger than the previous scheme because the probability for breaking the proposed scheme using the off-line password guessing and impersonation attack is less than probability for breaking Lee's scheme.","PeriodicalId":338866,"journal":{"name":"The 2nd European Symposium on Computer and Communications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127420674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper introduces the research process of using molecular dynamics theory to simulate the thermal conductivity of two-dimensional IV A materials represented by graphene. The major works include using Materials Studio to construct the material model, importing the model into Lammps for simulation calculation, and using non-equilibrium molecular dynamics (NEMD) to calculate the thermal conductivity. The relationship between thermal conductivity and model size of several two-dimensional IV A materials is summarized. With the increase of size, the thermal conductivity increases. Moreover, the thermal conductivity increases approximately linearly with the increase of length and nonlinear with the increase of width, which shows that the first derivative of the function of thermal conductivity and width decreases gradually with the increase of width, but it is always positive. The relationship between thermal conductivity and temperature is summarized as well. The thermal conductivity increases with the increase of temperature, and the thermal conductivity has the maximum value with respect to the first derivative of the initial temperature around 330K, but some literatures have given the opposite experimental results: the thermal conductivity decreases with the increase of temperature. The molecular dynamics simulation process and influencing factors of thermal conductivity of two-dimensional IV A materials that are studied in this paper play a certain role in the field of low-dimensional thermal properties.
{"title":"Research Progress on Thermal Conductivity of Two-dimensional IV A Materials Based on Molecular Dynamics Simulation","authors":"Yucheng Yang","doi":"10.1145/3478301.3478309","DOIUrl":"https://doi.org/10.1145/3478301.3478309","url":null,"abstract":"This paper introduces the research process of using molecular dynamics theory to simulate the thermal conductivity of two-dimensional IV A materials represented by graphene. The major works include using Materials Studio to construct the material model, importing the model into Lammps for simulation calculation, and using non-equilibrium molecular dynamics (NEMD) to calculate the thermal conductivity. The relationship between thermal conductivity and model size of several two-dimensional IV A materials is summarized. With the increase of size, the thermal conductivity increases. Moreover, the thermal conductivity increases approximately linearly with the increase of length and nonlinear with the increase of width, which shows that the first derivative of the function of thermal conductivity and width decreases gradually with the increase of width, but it is always positive. The relationship between thermal conductivity and temperature is summarized as well. The thermal conductivity increases with the increase of temperature, and the thermal conductivity has the maximum value with respect to the first derivative of the initial temperature around 330K, but some literatures have given the opposite experimental results: the thermal conductivity decreases with the increase of temperature. The molecular dynamics simulation process and influencing factors of thermal conductivity of two-dimensional IV A materials that are studied in this paper play a certain role in the field of low-dimensional thermal properties.","PeriodicalId":338866,"journal":{"name":"The 2nd European Symposium on Computer and Communications","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128079735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the common use of credit cards in today's transactions, the related fraudulent behavior inevitably occurs and causes considerable loss of money. To solve this problem, our work used a dataset that contains legal credit card transactions as well as fraud transactions to find an effective solution. In this paper, through processing and analyzing the transaction data, the data was discovered to be unbalanced, so stratified sampling and oversampling were performed to achieve a more reliable analysis of the unbalanced dataset. Meanwhile, due to the randomness of sampling, the cross-validated were used for the final model selection. Further, we utilized five algorithms to build models which contains statistical machine learning model and deep learning model. To obtain optimal performance of the models, hyperparameter tuning was performed for the five classifiers. Finally, the results indicate that the optimal model was XGBoost, and its performance can be verified in a real-life scenario in the future.
{"title":"Decision Analysis and Prediction Based on Credit Card Fraud Data","authors":"Deshan Huang, Yu Lin, Zhaoxing Weng, Jiajie Xiong","doi":"10.1145/3478301.3478305","DOIUrl":"https://doi.org/10.1145/3478301.3478305","url":null,"abstract":"With the common use of credit cards in today's transactions, the related fraudulent behavior inevitably occurs and causes considerable loss of money. To solve this problem, our work used a dataset that contains legal credit card transactions as well as fraud transactions to find an effective solution. In this paper, through processing and analyzing the transaction data, the data was discovered to be unbalanced, so stratified sampling and oversampling were performed to achieve a more reliable analysis of the unbalanced dataset. Meanwhile, due to the randomness of sampling, the cross-validated were used for the final model selection. Further, we utilized five algorithms to build models which contains statistical machine learning model and deep learning model. To obtain optimal performance of the models, hyperparameter tuning was performed for the five classifiers. Finally, the results indicate that the optimal model was XGBoost, and its performance can be verified in a real-life scenario in the future.","PeriodicalId":338866,"journal":{"name":"The 2nd European Symposium on Computer and Communications","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133066975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In a continuous integration (CI) environment, software developers can frequently integrate the new or modified code to codebase and ensure the correctness of the modification by performing regression testing (RT). RT generally takes long time but the time period allocated for it is limited in practice. A CI environment can adopt test suite reduction (TSR) to improve RT and accelerate debugging by performing spectrum-based fault localization (SBFL). Yet, different TSR techniques will achieve different program test spectra and SBFL will operate based on these test spectra. Thus, the selection of TSR techniques has influence on both the effectiveness of the fault localization (FL) and the length of a CI period. If developers want to improve the CI by including TSR and SBFL, they have to choose a suitable TSR technique to maximize the FL effectiveness and minimize the CI period length. In this paper, we consider a TSR technique as the combination of the TSR strategy, the metric to evaluate test cases, and the coverage granularity level. We will analyze the impact caused by these three factors. Our study covers three TSR strategies, four metrics to evaluate test cases, and three coverage granularity levels, thus resulting in 36 TSR techniques. We investigate how each of the 36 TSR techniques affects the FL effectiveness and the CI period length by conducting the experiment based on seven subject programs that have been frequently adopted in the relevant studies. The experiment results indicate that performing TSR at branch coverage is recommended for optimizing the FL effectiveness while the HGS algorithm is suggested if the CI period length is developers’ main concern.
{"title":"Empirically Comparing the Test Suite Reduction Techniques in Continuous Integration Process: How Test Suite Reduction Impacts Fault Localization","authors":"Jutarporn Intasara, Chu-Ti Lin","doi":"10.1145/3478301.3478304","DOIUrl":"https://doi.org/10.1145/3478301.3478304","url":null,"abstract":"In a continuous integration (CI) environment, software developers can frequently integrate the new or modified code to codebase and ensure the correctness of the modification by performing regression testing (RT). RT generally takes long time but the time period allocated for it is limited in practice. A CI environment can adopt test suite reduction (TSR) to improve RT and accelerate debugging by performing spectrum-based fault localization (SBFL). Yet, different TSR techniques will achieve different program test spectra and SBFL will operate based on these test spectra. Thus, the selection of TSR techniques has influence on both the effectiveness of the fault localization (FL) and the length of a CI period. If developers want to improve the CI by including TSR and SBFL, they have to choose a suitable TSR technique to maximize the FL effectiveness and minimize the CI period length. In this paper, we consider a TSR technique as the combination of the TSR strategy, the metric to evaluate test cases, and the coverage granularity level. We will analyze the impact caused by these three factors. Our study covers three TSR strategies, four metrics to evaluate test cases, and three coverage granularity levels, thus resulting in 36 TSR techniques. We investigate how each of the 36 TSR techniques affects the FL effectiveness and the CI period length by conducting the experiment based on seven subject programs that have been frequently adopted in the relevant studies. The experiment results indicate that performing TSR at branch coverage is recommended for optimizing the FL effectiveness while the HGS algorithm is suggested if the CI period length is developers’ main concern.","PeriodicalId":338866,"journal":{"name":"The 2nd European Symposium on Computer and Communications","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133413495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yogi Tri Prasetyo, Patrick Viquiera, Christine M. Pudadera, Myka M. Sobremonte
Mobile gaming is part and parcel of almost every student's life. The purpose of this study was to investigate the effects of online mobile games on achievement motivation, social support, and personal achievements by utilizing a structural equation modeling (SEM) approach. 254 participants were voluntary answered an online questionnaire which consist of 23 questions. SEM showed that gaming habits had a significant effect on perceived stress which subsequently affected achievement motivation, social support, and personal achievements. The SEM construct can be a theoretical foundation which would be very beneficial for psychologists, academicians, and even educators.
{"title":"The Effect of Gaming Habits on Achievement Motivation, Social Support, and Personal Achievements: A Structural Equation Modeling Approach","authors":"Yogi Tri Prasetyo, Patrick Viquiera, Christine M. Pudadera, Myka M. Sobremonte","doi":"10.1145/3478301.3478310","DOIUrl":"https://doi.org/10.1145/3478301.3478310","url":null,"abstract":"Mobile gaming is part and parcel of almost every student's life. The purpose of this study was to investigate the effects of online mobile games on achievement motivation, social support, and personal achievements by utilizing a structural equation modeling (SEM) approach. 254 participants were voluntary answered an online questionnaire which consist of 23 questions. SEM showed that gaming habits had a significant effect on perceived stress which subsequently affected achievement motivation, social support, and personal achievements. The SEM construct can be a theoretical foundation which would be very beneficial for psychologists, academicians, and even educators.","PeriodicalId":338866,"journal":{"name":"The 2nd European Symposium on Computer and Communications","volume":"459 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116773313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Real-time and accurate prediction of hospital outpatients is an important basis for the hospital to resolve the current contradiction between doctors and patients. However, the traditional hospital outpatients cannot accurately predict the data and reveal the internal laws of its time series, which cannot effectively adjust the treatment resources. This paper proposes the new particle swarm optimization (PSO) algorithm to optimize the BP neural network to predict outpatient timing. Specifically, it uses improved adaptive acceleration factor and inertia weight to iteratively optimize weight values and threshold values of the BP neural network, trains the BP neural network model, and then conducts calculation work. Its results are compared with those of the BP neural network optimized by the standard particle swarm optimization algorithm and the traditional BP neural network model respectively. The data comparison results show that new prediction accuracy is significantly improved and iterative calculation is very stable, therefore the improved particle swarm optimization BP neural network model can better predict the trend of hospital outpatient flow over time.
{"title":"Improved adaptive particle swarm for BP neural network optimization in hospital outpatient volume prediction","authors":"Yan-Bo Yang, Qin Zhang, Biaobiao Zhang","doi":"10.1145/3478301.3478307","DOIUrl":"https://doi.org/10.1145/3478301.3478307","url":null,"abstract":"Real-time and accurate prediction of hospital outpatients is an important basis for the hospital to resolve the current contradiction between doctors and patients. However, the traditional hospital outpatients cannot accurately predict the data and reveal the internal laws of its time series, which cannot effectively adjust the treatment resources. This paper proposes the new particle swarm optimization (PSO) algorithm to optimize the BP neural network to predict outpatient timing. Specifically, it uses improved adaptive acceleration factor and inertia weight to iteratively optimize weight values and threshold values of the BP neural network, trains the BP neural network model, and then conducts calculation work. Its results are compared with those of the BP neural network optimized by the standard particle swarm optimization algorithm and the traditional BP neural network model respectively. The data comparison results show that new prediction accuracy is significantly improved and iterative calculation is very stable, therefore the improved particle swarm optimization BP neural network model can better predict the trend of hospital outpatient flow over time.","PeriodicalId":338866,"journal":{"name":"The 2nd European Symposium on Computer and Communications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131182786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. Wu, Xiaoyun Guo, Cheng Xu, H. Qi, Yanxu Liu, Jianmin Zhao, Enwen Hu
Oriented towards the requirements for reliable positioning and navigation of aircraft under the condition of rejection of navigation satellite, we proposed cross-domain guide positioning methods based on multi-layer network coverage, and designed the relative positioning network construction methods between ground nodes, aircrafts and ground nodes, aircrafts and target nodes. Combined with the relative positioning network characteristics of each layer, at the same time, considered the computational complexity and location accuracy, the self-established multi-point coordinate system method and the robust location method are proposed.
{"title":"Cross-Domain Guidance Positioning Method Based on Multi-Layer Network Structure","authors":"C. Wu, Xiaoyun Guo, Cheng Xu, H. Qi, Yanxu Liu, Jianmin Zhao, Enwen Hu","doi":"10.1145/3478301.3478306","DOIUrl":"https://doi.org/10.1145/3478301.3478306","url":null,"abstract":"Oriented towards the requirements for reliable positioning and navigation of aircraft under the condition of rejection of navigation satellite, we proposed cross-domain guide positioning methods based on multi-layer network coverage, and designed the relative positioning network construction methods between ground nodes, aircrafts and ground nodes, aircrafts and target nodes. Combined with the relative positioning network characteristics of each layer, at the same time, considered the computational complexity and location accuracy, the self-established multi-point coordinate system method and the robust location method are proposed.","PeriodicalId":338866,"journal":{"name":"The 2nd European Symposium on Computer and Communications","volume":"191 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115621879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Madushi Hasara Pathmaperuma, Y. Rahulamathavan, Safak Dogan, Ahmet M. Kondoz
Mobile users install different types of applications on their mobile devices based on their interests and needs and perform various activities on them (known as in-app activities). In this paper, we demonstrate that a passive eavesdropper can identify fine grained in-app activities by analysing encrypted network traffic information obtained by sniffing a Wireless Local Area Network (WLAN). Even though encryption protocols are used to provide security over Internet communications, side channel data is still leaked from encrypted traffic. We utilise this data (frame length, inter arrival time and direction) to identify the in-app activities. Further as a first study of its kind, we show that it is possible to identify in-app activities accurately by observing a very small subset of traffic, rather than observing the entire transaction of an activity as presented in existing literature. To reach these observations, we evaluated 51 in-app activities from three popular social networking apps and identified more than 85% of them correctly using the Bayes Net machine learning algorithm.
{"title":"User Mobile App Encrypted Activity Detection","authors":"Madushi Hasara Pathmaperuma, Y. Rahulamathavan, Safak Dogan, Ahmet M. Kondoz","doi":"10.1145/3478301.3478303","DOIUrl":"https://doi.org/10.1145/3478301.3478303","url":null,"abstract":"Mobile users install different types of applications on their mobile devices based on their interests and needs and perform various activities on them (known as in-app activities). In this paper, we demonstrate that a passive eavesdropper can identify fine grained in-app activities by analysing encrypted network traffic information obtained by sniffing a Wireless Local Area Network (WLAN). Even though encryption protocols are used to provide security over Internet communications, side channel data is still leaked from encrypted traffic. We utilise this data (frame length, inter arrival time and direction) to identify the in-app activities. Further as a first study of its kind, we show that it is possible to identify in-app activities accurately by observing a very small subset of traffic, rather than observing the entire transaction of an activity as presented in existing literature. To reach these observations, we evaluated 51 in-app activities from three popular social networking apps and identified more than 85% of them correctly using the Bayes Net machine learning algorithm.","PeriodicalId":338866,"journal":{"name":"The 2nd European Symposium on Computer and Communications","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128363614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The 2nd European Symposium on Computer and Communications","authors":"","doi":"10.1145/3478301","DOIUrl":"https://doi.org/10.1145/3478301","url":null,"abstract":"","PeriodicalId":338866,"journal":{"name":"The 2nd European Symposium on Computer and Communications","volume":"229 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123302316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Data mining has been a popular branch of computer science in recent years with great impact in different areas. It is a technique to find the inner connection, the unexpected pattern from a large amount of data and to obtain certain broad conclusions from it. Currently, many data mining techniques are developed and used in order to process data from various aspects and to improve the speed and accuracy of data analysis. Considering that data mining techniques combine knowledge from multiple domains, this article will present an overview of temporal techniques for data mining.
{"title":"Analysis of Techniques for Data Mining","authors":"Ke Zhu","doi":"10.1145/3478301.3478308","DOIUrl":"https://doi.org/10.1145/3478301.3478308","url":null,"abstract":"Data mining has been a popular branch of computer science in recent years with great impact in different areas. It is a technique to find the inner connection, the unexpected pattern from a large amount of data and to obtain certain broad conclusions from it. Currently, many data mining techniques are developed and used in order to process data from various aspects and to improve the speed and accuracy of data analysis. Considering that data mining techniques combine knowledge from multiple domains, this article will present an overview of temporal techniques for data mining.","PeriodicalId":338866,"journal":{"name":"The 2nd European Symposium on Computer and Communications","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114752234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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