Pub Date : 2023-05-23DOI: 10.1109/SERA57763.2023.10197670
Suhan Jiang, Jie Wu, Fei Zuo, A. Mei
Payment Channel Networks (PCNs) have been introduced as a viable solution to the scalability problem of the popular blockchain. In PCNs, a payment channel allows its end nodes to pay each other without publishing every transaction to the blockchain. A transaction can be routed in the network if there is a path of channels with sufficient funds, and the intermediate routing nodes can ask the transaction sender for a compensatory fee. However, a channel may eventually become depleted and cannot support further payments in a certain direction, as transaction flows from that direction is heavier than flows from the other direction. In this paper, we discuss a PCN node’s possible roles and objectives, and analyze the strategies nodes should take under different roles by considering nodes’ benefits and the network’s performance. Then, we examine two basic network structures (ring and chord) and determine the constraints under which they constitute a Nash equilibrium. Based on the theoretical results, we propose a balance-aware fee-incentivized routing algorithm to guarantee cost-efficient routing, fair fee charging, and the network’s long lasting good performance in general PCNs. Testbed-based evaluation is conducted to validate our theoretical results and to show the feasibility of our proposed approach.
{"title":"Balance-aware Cost-efficient Routing in the Payment Channel Network","authors":"Suhan Jiang, Jie Wu, Fei Zuo, A. Mei","doi":"10.1109/SERA57763.2023.10197670","DOIUrl":"https://doi.org/10.1109/SERA57763.2023.10197670","url":null,"abstract":"Payment Channel Networks (PCNs) have been introduced as a viable solution to the scalability problem of the popular blockchain. In PCNs, a payment channel allows its end nodes to pay each other without publishing every transaction to the blockchain. A transaction can be routed in the network if there is a path of channels with sufficient funds, and the intermediate routing nodes can ask the transaction sender for a compensatory fee. However, a channel may eventually become depleted and cannot support further payments in a certain direction, as transaction flows from that direction is heavier than flows from the other direction. In this paper, we discuss a PCN node’s possible roles and objectives, and analyze the strategies nodes should take under different roles by considering nodes’ benefits and the network’s performance. Then, we examine two basic network structures (ring and chord) and determine the constraints under which they constitute a Nash equilibrium. Based on the theoretical results, we propose a balance-aware fee-incentivized routing algorithm to guarantee cost-efficient routing, fair fee charging, and the network’s long lasting good performance in general PCNs. Testbed-based evaluation is conducted to validate our theoretical results and to show the feasibility of our proposed approach.","PeriodicalId":211080,"journal":{"name":"2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126464298","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 : 2023-05-23DOI: 10.1109/SERA57763.2023.10197758
Md. Fazle Rabbi, Arifa I. Champa, M. Zibran
Phishing emails, a type of cyberattack using fake emails, are difficult to recognize due to sophisticated techniques employed by attackers. In this paper, we use a natural language processing (NLP) and machine learning (ML) based approach for detecting phishing emails. We compare the efficacy of six different ML algorithms for the purpose. An empirical evaluation on two public datasets demonstrates that our approach detects phishing emails with high accuracy, precision, and recall. The findings from this work are useful in devising more efficient techniques for recognizing and preventing phishing attacks.
{"title":"Phishy? Detecting Phishing Emails Using ML and NLP","authors":"Md. Fazle Rabbi, Arifa I. Champa, M. Zibran","doi":"10.1109/SERA57763.2023.10197758","DOIUrl":"https://doi.org/10.1109/SERA57763.2023.10197758","url":null,"abstract":"Phishing emails, a type of cyberattack using fake emails, are difficult to recognize due to sophisticated techniques employed by attackers. In this paper, we use a natural language processing (NLP) and machine learning (ML) based approach for detecting phishing emails. We compare the efficacy of six different ML algorithms for the purpose. An empirical evaluation on two public datasets demonstrates that our approach detects phishing emails with high accuracy, precision, and recall. The findings from this work are useful in devising more efficient techniques for recognizing and preventing phishing attacks.","PeriodicalId":211080,"journal":{"name":"2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123368022","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 : 2023-05-23DOI: 10.1109/SERA57763.2023.10197691
K. Nishimatsu, A. Inoue
The purpose of this study is to understand mobile user behavior for collaboration services, especially financial services, offered by mobile carriers in Japan. Mobile phone market in Japan remains highly competitive, mobile carriers are focusing on introducing various types of collaboration services other than mobile services. In recent years, loyalty programs based on collaboration services has become an important decision-making factor in mobile carrier choice behavior. This is because loyalty programs are increasingly allowing users to directly use the reward points they have earned as electronic money to purchase various products and other goods, and users can choose the loyalty program that best suits their lifestyle and benefits. With the spread of smartphones, telecommunication services and financial services are increasingly collaborating, and loyalty program is becoming important tool for retaining users. Using survey data, we analyze and evaluate the impact of financial services on the preferences of mobile carriers. The results showed that financial services were effective in retaining users.
{"title":"Mobile User Analysis Considering Collaboration with Financial Services","authors":"K. Nishimatsu, A. Inoue","doi":"10.1109/SERA57763.2023.10197691","DOIUrl":"https://doi.org/10.1109/SERA57763.2023.10197691","url":null,"abstract":"The purpose of this study is to understand mobile user behavior for collaboration services, especially financial services, offered by mobile carriers in Japan. Mobile phone market in Japan remains highly competitive, mobile carriers are focusing on introducing various types of collaboration services other than mobile services. In recent years, loyalty programs based on collaboration services has become an important decision-making factor in mobile carrier choice behavior. This is because loyalty programs are increasingly allowing users to directly use the reward points they have earned as electronic money to purchase various products and other goods, and users can choose the loyalty program that best suits their lifestyle and benefits. With the spread of smartphones, telecommunication services and financial services are increasingly collaborating, and loyalty program is becoming important tool for retaining users. Using survey data, we analyze and evaluate the impact of financial services on the preferences of mobile carriers. The results showed that financial services were effective in retaining users.","PeriodicalId":211080,"journal":{"name":"2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132561052","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 : 2023-05-23DOI: 10.1109/SERA57763.2023.10197683
Hyesoo Shin, Eunjo Jang, Sojeong Kim, Ki Yong Lee
Graph Neural Networks (GNNs) are powerful tools for analyzing graph-structured data in various fields because of their great expressive power for graph data. They use a message-passing mechanism to update node embeddings, which are then used for tasks such as node classification and link prediction. Recently, node embeddings have also been used in research on graph node clustering, which aims to group similar nodes based on their features and graph topology. However, traditional methods for node clustering have a limitation in that GNNs only focus on generating node embeddings without considering the ultimate objective of clustering. To address this issue, a novel technique called "Deep Clustering" has been proposed, which integrates both node embedding and clustering stages. This requires defining a new loss function by simultaneously minimizing the GNN loss and the clustering loss. Our proposed loss function incorporates not only the distance within clusters but also the distance between clusters by applying the Silhouette coefficient, which enables us to achieve better clustering results. In this paper, we propose a Silhouette-based Deep Clustering with GNN (SDCG) to more effectively cluster nodes in a graph by iteratively training the embedding model to produce embedding vectors with improved clustering results. Through extensive experiments, we demonstrate that SDCG outperforms the conventional approach of performing embedding and clustering independently.
{"title":"SDCG: Silhouette-based Deep Clustering with GNN for Improved Graph Node Clustering","authors":"Hyesoo Shin, Eunjo Jang, Sojeong Kim, Ki Yong Lee","doi":"10.1109/SERA57763.2023.10197683","DOIUrl":"https://doi.org/10.1109/SERA57763.2023.10197683","url":null,"abstract":"Graph Neural Networks (GNNs) are powerful tools for analyzing graph-structured data in various fields because of their great expressive power for graph data. They use a message-passing mechanism to update node embeddings, which are then used for tasks such as node classification and link prediction. Recently, node embeddings have also been used in research on graph node clustering, which aims to group similar nodes based on their features and graph topology. However, traditional methods for node clustering have a limitation in that GNNs only focus on generating node embeddings without considering the ultimate objective of clustering. To address this issue, a novel technique called \"Deep Clustering\" has been proposed, which integrates both node embedding and clustering stages. This requires defining a new loss function by simultaneously minimizing the GNN loss and the clustering loss. Our proposed loss function incorporates not only the distance within clusters but also the distance between clusters by applying the Silhouette coefficient, which enables us to achieve better clustering results. In this paper, we propose a Silhouette-based Deep Clustering with GNN (SDCG) to more effectively cluster nodes in a graph by iteratively training the embedding model to produce embedding vectors with improved clustering results. Through extensive experiments, we demonstrate that SDCG outperforms the conventional approach of performing embedding and clustering independently.","PeriodicalId":211080,"journal":{"name":"2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116724559","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 : 2023-05-23DOI: 10.1109/SERA57763.2023.10197712
Achhiya Sultana, Mahmudul Islam, Mahady Hasan, F. Ahmed
A lot of information is spread by people in the social media to update their status and share crucial news with others. But the majority of these platforms don’t promptly validate the individuals or their posts and people aren’t able to identify the fake news manually. Therefore, there is a need for an automated system capable of detecting fake news. This research has proposed to build a model using four machine learning algorithms. The dataset employed in the experiment is a composite of two datasets containing almost equal amounts of true and fake news articles on politics. The preprocessing stages begin with cleaning the data by removing punctuation, tokenization, special characters, white spaces, redundant word elimination, numerals, and English letters followed by stemming and stop with data discretization. Then, we analyzed the collected data and 80% of the data has been used to train each model initially. After that, the four manifested classification algorithms are applied. For identifying fake news from news articles, meth-ods like Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting Classifier were used. The trained classifiers’ accuracy has been evaluated using the remaining 20% of the data. The results show that the decision tree model produces the best accuracy of 99.60% and gradient boosting of 99.55%. Besides, the random forest shows 99.10% along with the logistic regression 98.99%. Moreover, we have explored the best model to achieve the highest precision, recall, F1-score based on the confusion matrix’s outcome.
{"title":"Fake News Detection Using Machine Learning Techniques","authors":"Achhiya Sultana, Mahmudul Islam, Mahady Hasan, F. Ahmed","doi":"10.1109/SERA57763.2023.10197712","DOIUrl":"https://doi.org/10.1109/SERA57763.2023.10197712","url":null,"abstract":"A lot of information is spread by people in the social media to update their status and share crucial news with others. But the majority of these platforms don’t promptly validate the individuals or their posts and people aren’t able to identify the fake news manually. Therefore, there is a need for an automated system capable of detecting fake news. This research has proposed to build a model using four machine learning algorithms. The dataset employed in the experiment is a composite of two datasets containing almost equal amounts of true and fake news articles on politics. The preprocessing stages begin with cleaning the data by removing punctuation, tokenization, special characters, white spaces, redundant word elimination, numerals, and English letters followed by stemming and stop with data discretization. Then, we analyzed the collected data and 80% of the data has been used to train each model initially. After that, the four manifested classification algorithms are applied. For identifying fake news from news articles, meth-ods like Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting Classifier were used. The trained classifiers’ accuracy has been evaluated using the remaining 20% of the data. The results show that the decision tree model produces the best accuracy of 99.60% and gradient boosting of 99.55%. Besides, the random forest shows 99.10% along with the logistic regression 98.99%. Moreover, we have explored the best model to achieve the highest precision, recall, F1-score based on the confusion matrix’s outcome.","PeriodicalId":211080,"journal":{"name":"2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116472690","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 : 2023-05-23DOI: 10.1109/SERA57763.2023.10197794
Yuya Yokoyama
In order to solve the issues of mismatches between the intentions of questioners and respondents at Question and Answer (Q&A) sites, nine factors of impressions for Q&A statements were obtained through factor analysis applied to the results of impression evaluation experiments. Then through multiple regression analysis, factor scores were estimated by using the feature values of statements. The factor scores estimated and obtained were subsequently utilized for detecting respondents who are expected to appropriately answer a posted question. Nevertheless, up to now the meanings and contents of Q&A statements have not been taken into consideration. Therefore, this paper aims to consider the semantics between Q&A statements. The feature values are reviewed and narrowed down to syntactic information, closing sentence expressions, 2-gram, and word2vec. The analysis result conveys that all the trials show good estimation with the consideration of cross-validation. It has also been suggested that applying word2vec could play a vital role in estimating improved factor scores.
{"title":"Consideration of Semantics between Q&A Statements to Obtain Factor Score","authors":"Yuya Yokoyama","doi":"10.1109/SERA57763.2023.10197794","DOIUrl":"https://doi.org/10.1109/SERA57763.2023.10197794","url":null,"abstract":"In order to solve the issues of mismatches between the intentions of questioners and respondents at Question and Answer (Q&A) sites, nine factors of impressions for Q&A statements were obtained through factor analysis applied to the results of impression evaluation experiments. Then through multiple regression analysis, factor scores were estimated by using the feature values of statements. The factor scores estimated and obtained were subsequently utilized for detecting respondents who are expected to appropriately answer a posted question. Nevertheless, up to now the meanings and contents of Q&A statements have not been taken into consideration. Therefore, this paper aims to consider the semantics between Q&A statements. The feature values are reviewed and narrowed down to syntactic information, closing sentence expressions, 2-gram, and word2vec. The analysis result conveys that all the trials show good estimation with the consideration of cross-validation. It has also been suggested that applying word2vec could play a vital role in estimating improved factor scores.","PeriodicalId":211080,"journal":{"name":"2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131265970","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 : 2023-05-23DOI: 10.1109/SERA57763.2023.10197705
Erdan Wang, Pouria Tayebi, Yeong-Tae Song
In this paper, we explore the potential of utilizing Digital Twin (DT) technology for real-time data storage and processing in emergency healthcare. Focusing on Internet of Things (IoT) and cloud computing technologies, we investigate various enabling technologies, including cloud platforms, data transmission formats, and storage file formats, to develop a feasible DT storage solution for emergency healthcare. Through our analysis, we find Amazon AWS to be the most suitable cloud platform due to its sophisticated real-time data processing and analytical tools. Additionally, we determine that the MQTT protocol is suitable for real-time medical data transmission, and FHIR is the most appropriate medical file storage format for emergency healthcare situations.We propose a cloud-based DT storage solution, in which real-time medical data is transmitted to AWS IoT Core, processed by Kinesis Data Analytics, and stored securely in AWS HealthLake. Despite the feasibility of the proposed solution, challenges such as insufficient access control, lack of encryption, and vendor conformity must be addressed for successful practical implementation. Future work may involve incorporating Hyperledger Fabric technology and HTTPS protocol to enhance security, while the maturation of DT technology is expected to resolve vendor conformity issues. By addressing these challenges, our proposed DT storage solution has the potential to improve data accessibility and decision-making in emergency healthcare settings.
在本文中，我们探讨了利用数字孪生(DT)技术进行实时数据存储和处理在紧急医疗保健中的潜力。我们以物联网(IoT)和云计算技术为重点，研究各种使能技术，包括云平台、数据传输格式和存储文件格式，以开发一种可行的应急医疗DT存储解决方案。通过我们的分析，我们发现亚马逊AWS是最合适的云平台，因为它有成熟的实时数据处理和分析工具。此外，我们确定MQTT协议适合实时医疗数据传输，而FHIR是最适合紧急医疗状况的医疗文件存储格式。我们提出了一种基于云的DT存储解决方案，其中实时医疗数据传输到AWS IoT Core，由Kinesis data Analytics处理，并安全存储在AWS HealthLake中。尽管提出的解决方案是可行的，但是要想成功地实际实现，必须解决诸如访问控制不足、缺乏加密和供应商一致性等挑战。未来的工作可能涉及整合Hyperledger Fabric技术和HTTPS协议以增强安全性，而DT技术的成熟有望解决供应商一致性问题。通过解决这些挑战，我们提出的DT存储解决方案有可能改善紧急医疗保健环境中的数据可访问性和决策。
{"title":"Cloud-based Digital Twins Storage in Emergency Healthcare","authors":"Erdan Wang, Pouria Tayebi, Yeong-Tae Song","doi":"10.1109/SERA57763.2023.10197705","DOIUrl":"https://doi.org/10.1109/SERA57763.2023.10197705","url":null,"abstract":"In this paper, we explore the potential of utilizing Digital Twin (DT) technology for real-time data storage and processing in emergency healthcare. Focusing on Internet of Things (IoT) and cloud computing technologies, we investigate various enabling technologies, including cloud platforms, data transmission formats, and storage file formats, to develop a feasible DT storage solution for emergency healthcare. Through our analysis, we find Amazon AWS to be the most suitable cloud platform due to its sophisticated real-time data processing and analytical tools. Additionally, we determine that the MQTT protocol is suitable for real-time medical data transmission, and FHIR is the most appropriate medical file storage format for emergency healthcare situations.We propose a cloud-based DT storage solution, in which real-time medical data is transmitted to AWS IoT Core, processed by Kinesis Data Analytics, and stored securely in AWS HealthLake. Despite the feasibility of the proposed solution, challenges such as insufficient access control, lack of encryption, and vendor conformity must be addressed for successful practical implementation. Future work may involve incorporating Hyperledger Fabric technology and HTTPS protocol to enhance security, while the maturation of DT technology is expected to resolve vendor conformity issues. By addressing these challenges, our proposed DT storage solution has the potential to improve data accessibility and decision-making in emergency healthcare settings.","PeriodicalId":211080,"journal":{"name":"2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131463586","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 : 2023-05-23DOI: 10.1109/SERA57763.2023.10197770
Yoonsik Cheon, R. Lozano, Rajasoundarya Senthil Prabhu
Assertions are a crucial aspect of software development, serving as a way to validate conditions during code execution. They are expressed as Boolean expressions that must hold true for the code to run correctly and enforce design decisions and constraints. This paper introduces a new approach for creating assertions called design assertions. These assertions are generated from formally written design constraints and help to enforce important design decisions during code execution. The approach utilizes immutable library classes, specifically designed for writing design assertions, and converts design constraints written in the Object Constraint Language (OCL) into assert statements in the Dart language. The result is a more readable, maintainable, and reliable set of Dart assertions, providing a powerful tool for ensuring software design quality and integrity.
{"title":"A Library-Based Approach for Writing Design Assertions","authors":"Yoonsik Cheon, R. Lozano, Rajasoundarya Senthil Prabhu","doi":"10.1109/SERA57763.2023.10197770","DOIUrl":"https://doi.org/10.1109/SERA57763.2023.10197770","url":null,"abstract":"Assertions are a crucial aspect of software development, serving as a way to validate conditions during code execution. They are expressed as Boolean expressions that must hold true for the code to run correctly and enforce design decisions and constraints. This paper introduces a new approach for creating assertions called design assertions. These assertions are generated from formally written design constraints and help to enforce important design decisions during code execution. The approach utilizes immutable library classes, specifically designed for writing design assertions, and converts design constraints written in the Object Constraint Language (OCL) into assert statements in the Dart language. The result is a more readable, maintainable, and reliable set of Dart assertions, providing a powerful tool for ensuring software design quality and integrity.","PeriodicalId":211080,"journal":{"name":"2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133033336","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 : 2023-05-23DOI: 10.1109/SERA57763.2023.10197684
Jongyeop Kim, Jinki Kim, M. Sands, Seongsoo Kim
This study utilizes vibration-based signal analysis as a non-destructive testing technique that involves analyzing the vibration signals produced by a structure to detect possible defects or damage. The study aims to employ deep learning models to identify defects in a 3D-printed cantilever beam by analyzing the beam’s tip displacement given a random input signal generated by an electromagnetic shaker. This study is focused on the output signal without any information of the random input, which is common for structural health monitoring applications in practice. Additionally, the study has revealed that the number of times the test set is applied to the trained model significantly impacts the accuracy of the model’s consistent predictions.
{"title":"Deep Learning Model to Improve the Stability of Damage Identification via Output-only Signal","authors":"Jongyeop Kim, Jinki Kim, M. Sands, Seongsoo Kim","doi":"10.1109/SERA57763.2023.10197684","DOIUrl":"https://doi.org/10.1109/SERA57763.2023.10197684","url":null,"abstract":"This study utilizes vibration-based signal analysis as a non-destructive testing technique that involves analyzing the vibration signals produced by a structure to detect possible defects or damage. The study aims to employ deep learning models to identify defects in a 3D-printed cantilever beam by analyzing the beam’s tip displacement given a random input signal generated by an electromagnetic shaker. This study is focused on the output signal without any information of the random input, which is common for structural health monitoring applications in practice. Additionally, the study has revealed that the number of times the test set is applied to the trained model significantly impacts the accuracy of the model’s consistent predictions.","PeriodicalId":211080,"journal":{"name":"2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"272 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122770353","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 : 2023-05-23DOI: 10.1109/SERA57763.2023.10197774
John Mulo, Pu Tian, Adamu Hussaini, Hengshuo Liang, Wei Yu
The applications of machine learning (ML) in cyber-physical systems (CPS), such as the smart energy grid has increased significantly. While ML technology can be integrated into CPS, the security risk of ML technology has to be considered. In particular, adversarial examples provide inputs to a ML model with intentionally attached perturbations (noise) that could pose the model to make incorrect decisions. Perturbations are expected to be small or marginal so that adversarial examples could be invisible to humans, but can significantly affect the output of ML models. In this paper, we design a taxonomy to provide the problem space for investigating the adversarial example generation techniques based on state-of-the-art literature. We propose a three-dimensional framework containing three dimensions for adversarial attack scenarios (i.e., black-box, white-box, and gray-box), target type, and adversarial examples generation methods (gradient-based, score-based, decision-based, transfer- based, and others). Based on the designed taxonomy, we systematically review the existing research efforts on adversarial ML in representative CPS (i.e., transportation, healthcare, and energy). Furthermore, we provide one case study to demonstrate the impact of adversarial examples of attacks on a smart energy CPS deployment. The results indicate that the accuracy can decrease significantly from 92.62% to 55.42% with a 30% adversarial sample injection. Finally, we discuss potential countermeasures and future research directions for adversarial ML.
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