Pub Date : 2022-07-01DOI: 10.1109/ICCSM57214.2022.00017
Wattana Nontawong, Rachata Songchaisanguan, W. Chaikumming, P. Patumchat, S. Sala-ngamand, Kiaertisak Sriprateep
The purpose of this article was to simulate the structure of Purl, Jersey and Interlock weft knitted fabric structures in three dimensions. The components consisted of twisted yarn with a model of filament assembly using Computer Aided Design (CAD) for representing the weft knitted fabric structures. The variable parameters used in the simulation. The results from 3 models of weft knitted fabrics showed that there are more realistic structures than revealed by previous study. The strength of these weft knitted fabric models will be analysed by Computer Aided Engineering (CAE) in future work.
{"title":"Computer Geometric Modeling Approach of Weft Knitted fabric Structures","authors":"Wattana Nontawong, Rachata Songchaisanguan, W. Chaikumming, P. Patumchat, S. Sala-ngamand, Kiaertisak Sriprateep","doi":"10.1109/ICCSM57214.2022.00017","DOIUrl":"https://doi.org/10.1109/ICCSM57214.2022.00017","url":null,"abstract":"The purpose of this article was to simulate the structure of Purl, Jersey and Interlock weft knitted fabric structures in three dimensions. The components consisted of twisted yarn with a model of filament assembly using Computer Aided Design (CAD) for representing the weft knitted fabric structures. The variable parameters used in the simulation. The results from 3 models of weft knitted fabrics showed that there are more realistic structures than revealed by previous study. The strength of these weft knitted fabric models will be analysed by Computer Aided Engineering (CAE) in future work.","PeriodicalId":426673,"journal":{"name":"2022 6th International Conference on Computer, Software and Modeling (ICCSM)","volume":"227 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117150864","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}
Pub Date : 2022-07-01DOI: 10.1109/ICCSM57214.2022.00014
Junhao Luo, Ladon Shu
This paper organically combines VaR and ES models with extreme value theory on a computer data platform. This paper uses the Bootstrap method to give the confidence interval of VaR and ES estimated by the extreme value theory at a certain confidence level, and improves the confidence interval estimated by the likelihood ratio method. Finally, this paper uses the logarithmic daily rate of return of China's Shanghai Composite Index from December 19, 2006 to September 30, 2020 to conduct an empirical study to give the VaR and ES values and confidence intervals of the Shanghai Composite Index.
{"title":"Research on the Application of Economics Model in Network Course Design Based on Computer Data Platform","authors":"Junhao Luo, Ladon Shu","doi":"10.1109/ICCSM57214.2022.00014","DOIUrl":"https://doi.org/10.1109/ICCSM57214.2022.00014","url":null,"abstract":"This paper organically combines VaR and ES models with extreme value theory on a computer data platform. This paper uses the Bootstrap method to give the confidence interval of VaR and ES estimated by the extreme value theory at a certain confidence level, and improves the confidence interval estimated by the likelihood ratio method. Finally, this paper uses the logarithmic daily rate of return of China's Shanghai Composite Index from December 19, 2006 to September 30, 2020 to conduct an empirical study to give the VaR and ES values and confidence intervals of the Shanghai Composite Index.","PeriodicalId":426673,"journal":{"name":"2022 6th International Conference on Computer, Software and Modeling (ICCSM)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129876513","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}
Pub Date : 2022-07-01DOI: 10.1109/ICCSM57214.2022.00018
H. Tao, Li Bi-cheng, Lin Zheng-Chao
At present, most personality trait prediction studies mainly use the cooperative method, that is, using the scale to collect users' personality trait information. This method mainly has the disadvantages of strong subjectivity, limited quantity and quality, insufficient lasting stability and requiring users to cooperate. At the same time, the mainstream method uses the black box method of supervised learning, which belongs to the data-driven method and is not interpretable. Knowledge driven dictionary method is expected to solve these problems and realize non cooperative personality prediction. This paper proposes a method of constructing personality dictionary based on the combination of knowledge base and corpus. On the other hand, aiming at the unclear physical meaning of personality scoring algorithm in personality analysis using dictionary method, this paper proposes a personality scoring algorithm based on vocabulary weight and word frequency. The results show that the personality dictionary constructed by this method can ensure both timeliness and comprehensiveness in vocabulary. The experimental results show that the personality dictionary constructed by this method can ensure both timeliness and comprehensiveness in vocabulary. The average similarity between the predicted results of Weibo personality dictionary and the results of the scale is 61.98%, which is close to the results of BFM algorithm,which can effectively predict users' personality.
{"title":"Non-operative Personality Prediction Based on Knowledge Driven","authors":"H. Tao, Li Bi-cheng, Lin Zheng-Chao","doi":"10.1109/ICCSM57214.2022.00018","DOIUrl":"https://doi.org/10.1109/ICCSM57214.2022.00018","url":null,"abstract":"At present, most personality trait prediction studies mainly use the cooperative method, that is, using the scale to collect users' personality trait information. This method mainly has the disadvantages of strong subjectivity, limited quantity and quality, insufficient lasting stability and requiring users to cooperate. At the same time, the mainstream method uses the black box method of supervised learning, which belongs to the data-driven method and is not interpretable. Knowledge driven dictionary method is expected to solve these problems and realize non cooperative personality prediction. This paper proposes a method of constructing personality dictionary based on the combination of knowledge base and corpus. On the other hand, aiming at the unclear physical meaning of personality scoring algorithm in personality analysis using dictionary method, this paper proposes a personality scoring algorithm based on vocabulary weight and word frequency. The results show that the personality dictionary constructed by this method can ensure both timeliness and comprehensiveness in vocabulary. The experimental results show that the personality dictionary constructed by this method can ensure both timeliness and comprehensiveness in vocabulary. The average similarity between the predicted results of Weibo personality dictionary and the results of the scale is 61.98%, which is close to the results of BFM algorithm,which can effectively predict users' personality.","PeriodicalId":426673,"journal":{"name":"2022 6th International Conference on Computer, Software and Modeling (ICCSM)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125304255","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}
Pub Date : 2022-07-01DOI: 10.1109/ICCSM57214.2022.00022
Kaizhe Ding
With the development of Internet of Things (IoT), wearable devices have gradually become popular, and different new network architectures have also been proposed. Social Internet of Things (SIoT) is a newly proposed architecture that allows devices to realize social-like self-selection and connection. This project is aimed at wearable devices, and uses the Erdös-Rényi (ER) random model and general random number generation methods to simulate the topology based on the IoT architecture and the SIoT architecture, and analyze the closeness centrality and degree centrality of the nodes in the topology and the overall. Finally, it is found that the topology based on the SIoT architecture is better than the topology based on the IoT architecture in terms of node and overall network expansion. This project can provide a certain reference for future distributed IoT network research.
随着物联网(IoT)的发展，可穿戴设备逐渐普及，不同的新型网络架构也被提出。社交物联网(Social Internet of Things, SIoT)是一种新提出的架构，它允许设备实现类似社交的自我选择和连接。本项目针对可穿戴设备，采用Erdös-Rényi (ER)随机模型和通用随机数生成方法，对基于IoT架构和SIoT架构的拓扑进行仿真，分析拓扑和整体中节点的紧密中心性和度中心性。最后，发现基于SIoT架构的拓扑在节点扩展和整体网络扩展方面优于基于IoT架构的拓扑。本课题可为未来分布式物联网网络的研究提供一定的参考。
{"title":"Comparison of Topology Expansion of Wearable Devices Based on IoT and SIoT Architecture","authors":"Kaizhe Ding","doi":"10.1109/ICCSM57214.2022.00022","DOIUrl":"https://doi.org/10.1109/ICCSM57214.2022.00022","url":null,"abstract":"With the development of Internet of Things (IoT), wearable devices have gradually become popular, and different new network architectures have also been proposed. Social Internet of Things (SIoT) is a newly proposed architecture that allows devices to realize social-like self-selection and connection. This project is aimed at wearable devices, and uses the Erdös-Rényi (ER) random model and general random number generation methods to simulate the topology based on the IoT architecture and the SIoT architecture, and analyze the closeness centrality and degree centrality of the nodes in the topology and the overall. Finally, it is found that the topology based on the SIoT architecture is better than the topology based on the IoT architecture in terms of node and overall network expansion. This project can provide a certain reference for future distributed IoT network research.","PeriodicalId":426673,"journal":{"name":"2022 6th International Conference on Computer, Software and Modeling (ICCSM)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122914543","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}
Pub Date : 2022-07-01DOI: 10.1109/ICCSM57214.2022.00013
H. Vardhan, J. Sztipanovits
During the design process of an autonomous underwater vehicle (AUV), the pressure vessel has a critical role. The pressure vessel contains dry electronics, power sources, and other sensors that cannot be flooded. A traditional design approach for a pressure vessel design involves running multiple Finite Element Analysis (FEA) based simulations and optimizing the design to find the best suitable design which meets the requirement. Running these FEAs are computationally very costly for any optimization process and it becomes difficult to run even hundreds of evaluation. In such a case, a better approach is the surrogate design with the goal of replacing FEA-based prediction with some learning-based regressor. Once the surrogate is trained for a class of problem, then the learned response surface can be used to analyze the stress effect without running the FEA for that class of problem. The challenge of creating a surrogate for a class of problems is data generation. Since the process is computationally costly, it is not possible to densely sample the design space and the learning response surface on sparse data set becomes difficult. During experimentation, we observed that a Deep Learning-based surrogate outperforms other regression models on such sparse data. In the present work, we are utilizing the Deep Learning-based model to replace the costly finite element analysis-based simulation process. By creating the surrogate, we speed up the prediction on the other design much faster than direct Finite element Analysis. We also compared our DL-based surrogate with other classical Machine Learning (ML) based regression models (random forest and Gradient Boost regressor). We observed on the sparser data, the DL-based surrogate performs much better than other regression models.
{"title":"Deep Learning based FEA Surrogate for Sub-Sea Pressure Vessel","authors":"H. Vardhan, J. Sztipanovits","doi":"10.1109/ICCSM57214.2022.00013","DOIUrl":"https://doi.org/10.1109/ICCSM57214.2022.00013","url":null,"abstract":"During the design process of an autonomous underwater vehicle (AUV), the pressure vessel has a critical role. The pressure vessel contains dry electronics, power sources, and other sensors that cannot be flooded. A traditional design approach for a pressure vessel design involves running multiple Finite Element Analysis (FEA) based simulations and optimizing the design to find the best suitable design which meets the requirement. Running these FEAs are computationally very costly for any optimization process and it becomes difficult to run even hundreds of evaluation. In such a case, a better approach is the surrogate design with the goal of replacing FEA-based prediction with some learning-based regressor. Once the surrogate is trained for a class of problem, then the learned response surface can be used to analyze the stress effect without running the FEA for that class of problem. The challenge of creating a surrogate for a class of problems is data generation. Since the process is computationally costly, it is not possible to densely sample the design space and the learning response surface on sparse data set becomes difficult. During experimentation, we observed that a Deep Learning-based surrogate outperforms other regression models on such sparse data. In the present work, we are utilizing the Deep Learning-based model to replace the costly finite element analysis-based simulation process. By creating the surrogate, we speed up the prediction on the other design much faster than direct Finite element Analysis. We also compared our DL-based surrogate with other classical Machine Learning (ML) based regression models (random forest and Gradient Boost regressor). We observed on the sparser data, the DL-based surrogate performs much better than other regression models.","PeriodicalId":426673,"journal":{"name":"2022 6th International Conference on Computer, Software and Modeling (ICCSM)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128588927","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}
Pub Date : 2022-07-01DOI: 10.1109/ICCSM57214.2022.00010
Shaimaa Fadel, W. Abdelmoez, A. Saad
Addressing the importance of the requirements prioritization, recent research introduced ‘A value-based approach for reasoning with goal models’, which allows stakeholders to set an importance estimate for each element in a goal model, that relates the requirements with the business objectives. These estimates are then propagated by means of their relationships (dependencies, contributions and decompositions) to get a prioritized list of all requirements.In this paper, a methodology that extends the aforementioned technique is presented. It uses Goal Oriented Requirements Language (GRL) to prioritize requirements in an agile environment, taking into consideration the opinion of many Stakeholders. As the static value of the estimate is never accurate and does not give the stakeholders enough flexibility, the proposed methodology enables each stakeholder to provide for each requirement its importance as an estimated interval instead of a static value. Then, for each requirement the mean value of the estimation interval is calculated, the information asymmetry and confidence level per requirement are demonstrated, so that a prioritized list can then be generated. The analyst can choose to use the prioritized list based on one of these criteria. This methodology is demonstrated on a case study.
{"title":"Considering Multiple Stakeholders Perspectives for interval-based Goal Oriented Requirements Prioritization in agile development","authors":"Shaimaa Fadel, W. Abdelmoez, A. Saad","doi":"10.1109/ICCSM57214.2022.00010","DOIUrl":"https://doi.org/10.1109/ICCSM57214.2022.00010","url":null,"abstract":"Addressing the importance of the requirements prioritization, recent research introduced ‘A value-based approach for reasoning with goal models’, which allows stakeholders to set an importance estimate for each element in a goal model, that relates the requirements with the business objectives. These estimates are then propagated by means of their relationships (dependencies, contributions and decompositions) to get a prioritized list of all requirements.In this paper, a methodology that extends the aforementioned technique is presented. It uses Goal Oriented Requirements Language (GRL) to prioritize requirements in an agile environment, taking into consideration the opinion of many Stakeholders. As the static value of the estimate is never accurate and does not give the stakeholders enough flexibility, the proposed methodology enables each stakeholder to provide for each requirement its importance as an estimated interval instead of a static value. Then, for each requirement the mean value of the estimation interval is calculated, the information asymmetry and confidence level per requirement are demonstrated, so that a prioritized list can then be generated. The analyst can choose to use the prioritized list based on one of these criteria. This methodology is demonstrated on a case study.","PeriodicalId":426673,"journal":{"name":"2022 6th International Conference on Computer, Software and Modeling (ICCSM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116030882","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}
Pub Date : 2022-07-01DOI: 10.1109/ICCSM57214.2022.00015
Yuxin Jia, Wenzheng Ma, Dongxiao Fu
The dynamic compressive mechanical properties of 08F steel are numerically simulated by using quasi-static test and split Hopkinson pressure bar (SHPB) system, in order to improve the accuracy of finite element simulations in pulse shaper. The yield strength and Young's modulus of the material are determined by quasi-static compression test at the strain rate of 10-3s-1 the specimen size was Φ5 mm × 5 mm. The strain rate effect of the material is determined by split Hopkinson pressure bar (SHPB) system at the strain rates of 102 S-1 and 103 s-1, The Johnson-cook constitutive model parameters of 08F steel are analyzed and determined. PAM-CRASH is used to do numerical simulations with the deformation effect of 08F steel under four impact conditions. Compared with the actual test results, the numerically simulated results show that Johnson-cook constitutive simulation is suitable for the simulation of impact process of pulse shaper of 08F steel, which has obvious strain hardening property and strain rate sensitivity. The dynamic stress-strain curves is regressed based on the test results, which can be used as a reference for relevant research.
为了提高脉冲成形器有限元模拟的精度，采用准静态试验和分离式霍普金森压杆(SHPB)系统对08F钢的动态压缩力学性能进行了数值模拟。材料的屈服强度和杨氏模量采用准静态压缩试验，应变速率为10-3s-1，试样尺寸为Φ5 mm × 5mm。采用分离式霍普金森压杆(SHPB)系统研究了102s -1和103s -1应变速率下材料的应变速率效应，并对08F钢的Johnson-cook本构模型参数进行了分析和确定。采用PAM-CRASH软件对08F钢在四种冲击条件下的变形效果进行了数值模拟。数值模拟结果表明，Johnson-cook本构模拟适用于08F钢脉冲成形器冲击过程的模拟，具有明显的应变硬化性能和应变速率敏感性。根据试验结果对动态应力-应变曲线进行回归，可为相关研究提供参考。
{"title":"Parameter Test and Numerical Simulation of Dynamic Constitutive Model for 08F Steel","authors":"Yuxin Jia, Wenzheng Ma, Dongxiao Fu","doi":"10.1109/ICCSM57214.2022.00015","DOIUrl":"https://doi.org/10.1109/ICCSM57214.2022.00015","url":null,"abstract":"The dynamic compressive mechanical properties of 08F steel are numerically simulated by using quasi-static test and split Hopkinson pressure bar (SHPB) system, in order to improve the accuracy of finite element simulations in pulse shaper. The yield strength and Young's modulus of the material are determined by quasi-static compression test at the strain rate of 10-3s-1 the specimen size was Φ5 mm × 5 mm. The strain rate effect of the material is determined by split Hopkinson pressure bar (SHPB) system at the strain rates of 102 S-1 and 103 s-1, The Johnson-cook constitutive model parameters of 08F steel are analyzed and determined. PAM-CRASH is used to do numerical simulations with the deformation effect of 08F steel under four impact conditions. Compared with the actual test results, the numerically simulated results show that Johnson-cook constitutive simulation is suitable for the simulation of impact process of pulse shaper of 08F steel, which has obvious strain hardening property and strain rate sensitivity. The dynamic stress-strain curves is regressed based on the test results, which can be used as a reference for relevant research.","PeriodicalId":426673,"journal":{"name":"2022 6th International Conference on Computer, Software and Modeling (ICCSM)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116509436","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}
Pub Date : 2022-07-01DOI: 10.1109/ICCSM57214.2022.00009
B. Johnson, R. Simha
Despite significant work in the area of software complexity, there are still numerous unanswered questions about the sources and locations of complexity and its relationship to software design and programming language features. In this paper, we attempt to illuminate these questions by applying code-agnostic statistical dimensionality reduction techniques to a large dataset of 3000 popular open source Java programs.We analyze our set of projects to determine key attributes of Java program composition and complexity, using standard metrics from previous work. We apply two proven dimensionality reduction techniques, Principle Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) to explore the relationships between complexity models and program composition. We find support for three primary sources of Java software complexity and note that particular projects are most often associated primarily with one variety. Our results have potential implications for source code analysis and programming language design.
{"title":"Using Dimensionality Reduction Techniques to Understand the Sources of Software Complexity","authors":"B. Johnson, R. Simha","doi":"10.1109/ICCSM57214.2022.00009","DOIUrl":"https://doi.org/10.1109/ICCSM57214.2022.00009","url":null,"abstract":"Despite significant work in the area of software complexity, there are still numerous unanswered questions about the sources and locations of complexity and its relationship to software design and programming language features. In this paper, we attempt to illuminate these questions by applying code-agnostic statistical dimensionality reduction techniques to a large dataset of 3000 popular open source Java programs.We analyze our set of projects to determine key attributes of Java program composition and complexity, using standard metrics from previous work. We apply two proven dimensionality reduction techniques, Principle Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) to explore the relationships between complexity models and program composition. We find support for three primary sources of Java software complexity and note that particular projects are most often associated primarily with one variety. Our results have potential implications for source code analysis and programming language design.","PeriodicalId":426673,"journal":{"name":"2022 6th International Conference on Computer, Software and Modeling (ICCSM)","volume":"14 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113936368","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}
Pub Date : 2022-07-01DOI: 10.1109/iccsm57214.2022.00021
Sara Abbaspour Asadollah
Industrial cybersecurity has risen as an important topic of research nowadays. The heavy connectivity by the Internet of Things (IoT) and the growth of cyberattacks against industrial assets cause this risen and attract attention to the cybersecurity field. While fostering current software applications and use-cases, the ubiquitous access to the Internet has also exposed operational technologies to new and challenging security threats that need to be addressed. As the number of attacks increases, their visibility decreases. An attack can modify the Cyber-Physical Systems (CPSs) quality to avoid proper quality assessment. They can disrupt the system design process and adversely affect a product’s design purpose.This working progress paper presents our approach to modeling, analyzing, and mitigating cyberattacks in CPS. We model the normal behavior of the application as well as cyberattacks with the help of Microsoft Security Development Lifecycle (SDL) and threat modeling approach (STRIDE). Then verify the application and attacks model using a model checking tool and propose mitigation strategies to decrease the risk of vulnerabilities. The results can be used to improve the system design to overcome the vulnerabilities.
{"title":"Cyberattacks: Modeling, Analysis, and Mitigation","authors":"Sara Abbaspour Asadollah","doi":"10.1109/iccsm57214.2022.00021","DOIUrl":"https://doi.org/10.1109/iccsm57214.2022.00021","url":null,"abstract":"Industrial cybersecurity has risen as an important topic of research nowadays. The heavy connectivity by the Internet of Things (IoT) and the growth of cyberattacks against industrial assets cause this risen and attract attention to the cybersecurity field. While fostering current software applications and use-cases, the ubiquitous access to the Internet has also exposed operational technologies to new and challenging security threats that need to be addressed. As the number of attacks increases, their visibility decreases. An attack can modify the Cyber-Physical Systems (CPSs) quality to avoid proper quality assessment. They can disrupt the system design process and adversely affect a product’s design purpose.This working progress paper presents our approach to modeling, analyzing, and mitigating cyberattacks in CPS. We model the normal behavior of the application as well as cyberattacks with the help of Microsoft Security Development Lifecycle (SDL) and threat modeling approach (STRIDE). Then verify the application and attacks model using a model checking tool and propose mitigation strategies to decrease the risk of vulnerabilities. The results can be used to improve the system design to overcome the vulnerabilities.","PeriodicalId":426673,"journal":{"name":"2022 6th International Conference on Computer, Software and Modeling (ICCSM)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115854588","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}
Pub Date : 2022-07-01DOI: 10.1109/iccsm57214.2022.00012
Ahmad Sorour, A. Hamdy
During the recent COVID-19 outbreak, many educational institutions had to operate fully remotely and conduct examinations online. Conducting hands-on software lab exams online raises serious issues and concerns such as: 1) the heterogeneity of examinees’ personal computers, 2) the computers may not be powerful enough to run the required software for the hands-on exam, especially hardware intensive programs, 3) cheating and plagiarism are hardly controllable since examinees are using their personal computers and they can look up whatever information they need. The paper proposes a highly available and scalable software cloud architecture that utilizes modern cloud technologies, DevOps principles, and infrastructure as code tools of various categories to facilitate the construction of a highly available and scalable architectural solution that automates the delivery of software lab exams. Evaluation and results of the proposed architecture illustrate that a cloud instance that is preconfigured with all the required exam material can be instantiated and completely ready to use in an average of 149 seconds. Moreover, deploying the backend server on a Kubernetes Cluster allowed the system to automatically scale and handle sudden loads due to Kubernetes’ auto-scaling and self-healing features.
{"title":"DevOps and IaC to Automate the Delivery of Hands-On Software Lab Exams","authors":"Ahmad Sorour, A. Hamdy","doi":"10.1109/iccsm57214.2022.00012","DOIUrl":"https://doi.org/10.1109/iccsm57214.2022.00012","url":null,"abstract":"During the recent COVID-19 outbreak, many educational institutions had to operate fully remotely and conduct examinations online. Conducting hands-on software lab exams online raises serious issues and concerns such as: 1) the heterogeneity of examinees’ personal computers, 2) the computers may not be powerful enough to run the required software for the hands-on exam, especially hardware intensive programs, 3) cheating and plagiarism are hardly controllable since examinees are using their personal computers and they can look up whatever information they need. The paper proposes a highly available and scalable software cloud architecture that utilizes modern cloud technologies, DevOps principles, and infrastructure as code tools of various categories to facilitate the construction of a highly available and scalable architectural solution that automates the delivery of software lab exams. Evaluation and results of the proposed architecture illustrate that a cloud instance that is preconfigured with all the required exam material can be instantiated and completely ready to use in an average of 149 seconds. Moreover, deploying the backend server on a Kubernetes Cluster allowed the system to automatically scale and handle sudden loads due to Kubernetes’ auto-scaling and self-healing features.","PeriodicalId":426673,"journal":{"name":"2022 6th International Conference on Computer, Software and Modeling (ICCSM)","volume":"253 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133073253","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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