Pub Date : 2022-12-01DOI: 10.1109/QRS-C57518.2022.00017
Marcos Felipe, Haiping Xu
Despite the growing interest in blockchain technology, the scalability of blockchain storage has become a major issue for applications that require large amounts of on-chain data. In this paper, we propose a novel scalable storage scheme for consortium networks to manage the storage capacity required by data-rich blockchain applications. We establish network nodes as super peers or regular peers, where super peers can maintain old blockchain data in the form of historical blockchains. Regular peers maintain only the latest blockchain data stored in the current blockchain, but they can access any data in the historical blockchains by making queries to the super peers. We present procedures to build a historical blockchain and retrieve data from the historical blockchains and the current blockchain in a concurrent manner. Experimental results show that our scalable storage scheme using historical blockchains is feasible and effective in accessing and sharing healthcare data with image files.
{"title":"A Scalable Storage Scheme for On-Chain Big Data using Historical Blockchains","authors":"Marcos Felipe, Haiping Xu","doi":"10.1109/QRS-C57518.2022.00017","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00017","url":null,"abstract":"Despite the growing interest in blockchain technology, the scalability of blockchain storage has become a major issue for applications that require large amounts of on-chain data. In this paper, we propose a novel scalable storage scheme for consortium networks to manage the storage capacity required by data-rich blockchain applications. We establish network nodes as super peers or regular peers, where super peers can maintain old blockchain data in the form of historical blockchains. Regular peers maintain only the latest blockchain data stored in the current blockchain, but they can access any data in the historical blockchains by making queries to the super peers. We present procedures to build a historical blockchain and retrieve data from the historical blockchains and the current blockchain in a concurrent manner. Experimental results show that our scalable storage scheme using historical blockchains is feasible and effective in accessing and sharing healthcare data with image files.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120972559","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 : 2022-12-01DOI: 10.1109/QRS-C57518.2022.00064
Jiaxi Xu, Jingwei Shang, Zhichang Huang
Software Defect Prediction (SDP) predicts new defects through machine learning trained with historical defect data. The distribution of software defects is highly unbalanced, which hinders the construction of defect prediction models. In addition, previous studies were usually validated by public datasets based on code metrics instead of real-world data. In this work, SNA metrics and code metrics are computed on 9 representative real-world projects. A hybrid preprocessing approach for defect prediction named CFIWSE is proposed to improve SDP performance through feature selection, minority sample synthesis and noise reduction, consisting of CFS and IWSE. CFS uses correlation analysis and nearest neighbor theory for feature selection. IWSE utilizes information weights and edited nearest neighbor rule to alleviate overfitting and noise introduced from minority sample synthesis. The proposed method is verified by experiments on real-world data, and the contribution of the method components and parameter sensitivity are explored.
{"title":"CFIWSE: A Hybrid Preprocessing Approach for Defect Prediction on Imbalance Real-World Datasets","authors":"Jiaxi Xu, Jingwei Shang, Zhichang Huang","doi":"10.1109/QRS-C57518.2022.00064","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00064","url":null,"abstract":"Software Defect Prediction (SDP) predicts new defects through machine learning trained with historical defect data. The distribution of software defects is highly unbalanced, which hinders the construction of defect prediction models. In addition, previous studies were usually validated by public datasets based on code metrics instead of real-world data. In this work, SNA metrics and code metrics are computed on 9 representative real-world projects. A hybrid preprocessing approach for defect prediction named CFIWSE is proposed to improve SDP performance through feature selection, minority sample synthesis and noise reduction, consisting of CFS and IWSE. CFS uses correlation analysis and nearest neighbor theory for feature selection. IWSE utilizes information weights and edited nearest neighbor rule to alleviate overfitting and noise introduced from minority sample synthesis. The proposed method is verified by experiments on real-world data, and the contribution of the method components and parameter sensitivity are explored.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127103525","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 : 2022-12-01DOI: 10.1109/QRS-C57518.2022.00108
Lin Zhu, Luyi Bai, Xuesong Hao, Hongji Yang
Knowledge graph is used to represent the concepts, entities and relationships existing in the real world, which can be applied to many applications such as creative computing and recommendation system. Structurally, knowledge graph includes data layer and schema layer. Spatiotemporal knowledge graph extends the common knowledge graph to a certain extent, which is mainly reflected in the entity layer (data layer). Spatiotemporal knowledge graph includes temporal feature, spatial feature and spatiotemporal feature. In the pattern layer, spatiotemporal knowledge graph mainly adds concepts and relationships between concepts, which needs to be re-modeled. In this paper, as a spatiotemporal extension of the general description logic based on OWL logic, the spatiotemporal description logic (ST DL) is proposed to describe the spatiotemporal knowledge graph, and ST OWL is extended from three aspects: OWL class description, OWL axiom and OWL data type. Then, the corresponding transformation rules are proposed, and the instance is transformed from spatiotemporal ontology structure to spatiotemporal knowledge graph.
{"title":"A General Characterization of Representing Spatiotemporal Knowledge Graph based on OWL","authors":"Lin Zhu, Luyi Bai, Xuesong Hao, Hongji Yang","doi":"10.1109/QRS-C57518.2022.00108","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00108","url":null,"abstract":"Knowledge graph is used to represent the concepts, entities and relationships existing in the real world, which can be applied to many applications such as creative computing and recommendation system. Structurally, knowledge graph includes data layer and schema layer. Spatiotemporal knowledge graph extends the common knowledge graph to a certain extent, which is mainly reflected in the entity layer (data layer). Spatiotemporal knowledge graph includes temporal feature, spatial feature and spatiotemporal feature. In the pattern layer, spatiotemporal knowledge graph mainly adds concepts and relationships between concepts, which needs to be re-modeled. In this paper, as a spatiotemporal extension of the general description logic based on OWL logic, the spatiotemporal description logic (ST DL) is proposed to describe the spatiotemporal knowledge graph, and ST OWL is extended from three aspects: OWL class description, OWL axiom and OWL data type. Then, the corresponding transformation rules are proposed, and the instance is transformed from spatiotemporal ontology structure to spatiotemporal knowledge graph.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116551324","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 : 2022-12-01DOI: 10.1109/QRS-C57518.2022.00093
Raheela Chand, Saif Ur Rehman Khan, S. Hussain, Wen Wang
Context: Android games are gaining wide attention from users in recent years. However, the existing literature reports alarming statistics about banning popular and top-trending Android apps. The popular gaming apps have been removed from Google Play Store due to various user concerns. Objectives: The goal of this work is twofold: (i) to assist the researchers and practitioners in identifying the state-of-the-art challenges, constraints, and compliments about Android apps for future Android-specific studies, and (ii) to encourage active users' perspectives on the Android development process because usability remains a core deciding factor about the success or failure of Android apps. Method: To accomplish this, we introduce a novel open-source dataset, Top Trending Android apps with their user Reviews (TTAG+R) in GitHub. Results and Contributions: Briefly, TTAG+R presents information about 245 top trending Android Free Games, 97 top trending Android Grossing Games, and 52 top trending Android Paid Games with a total of 8,423 user reviews in 12 different. csv files. The main contributions of this paper are: (i) provides one-place comprehensive data on Android Apps, (ii) describes various features of Android apps and their user reviews, (iii) reports the updated and latest knowledge about Android apps, and (iv) provides the data in an unfiltered form so that researchers may not find difficulty in using this dataset in their data-driven experimentation. From a research implication viewpoint, the dataset supports: (i) understanding the usability characteristics of Android apps, (ii) discovering current trends and pitfalls in Android apps, and (iii) analyzing the Android financial market. Conclusion and Future Work: Thus, TTAG+R is freely available to the research community, and useful for future enhancements in the Android domain. In the future, we plan to keep the data up-to-date with the most recent information for the continued usage of the dataset.
背景:Android游戏近年来获得了用户的广泛关注。然而，现有的文献报告了令人震惊的关于禁止流行和热门Android应用的统计数据。由于各种用户的担忧，这些流行的游戏应用已经从Google Play Store下架。目的:这项工作的目标是双重的:(i)帮助研究人员和实践者识别最新的挑战，限制，以及对Android应用程序的赞美，为未来的Android特定研究做准备;(ii)鼓励活跃用户对Android开发过程的看法，因为可用性仍然是Android应用程序成功或失败的核心决定因素。方法:为了实现这一目标，我们引入了一个新的开源数据集，GitHub中的Top Trending Android应用及其用户评论(TTAG+R)。结果和贡献:总的来说，TTAG+R提供了245款热门Android免费游戏、97款热门Android畅销游戏和52款热门Android付费游戏的信息，总共有8423条用户评论。csv文件。本文的主要贡献是:(i)提供了Android应用程序的一站式综合数据，(ii)描述了Android应用程序的各种功能及其用户评论，(iii)报告了关于Android应用程序的最新和最新知识，(iv)以未经过滤的形式提供数据，以便研究人员在数据驱动的实验中使用该数据集时不会发现困难。从研究意义的角度来看，该数据集支持:(i)了解Android应用程序的可用性特征，(ii)发现Android应用程序的当前趋势和陷阱，以及(iii)分析Android金融市场。结论和未来的工作:因此，TTAG+R对研究社区是免费的，并且对Android领域的未来增强很有用。在未来，我们计划使用最新的信息来保持数据的更新，以便继续使用数据集。
{"title":"TTAG+R: A Dataset of Google Play Store's Top Trending Android Games and User Reviews","authors":"Raheela Chand, Saif Ur Rehman Khan, S. Hussain, Wen Wang","doi":"10.1109/QRS-C57518.2022.00093","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00093","url":null,"abstract":"Context: Android games are gaining wide attention from users in recent years. However, the existing literature reports alarming statistics about banning popular and top-trending Android apps. The popular gaming apps have been removed from Google Play Store due to various user concerns. Objectives: The goal of this work is twofold: (i) to assist the researchers and practitioners in identifying the state-of-the-art challenges, constraints, and compliments about Android apps for future Android-specific studies, and (ii) to encourage active users' perspectives on the Android development process because usability remains a core deciding factor about the success or failure of Android apps. Method: To accomplish this, we introduce a novel open-source dataset, Top Trending Android apps with their user Reviews (TTAG+R) in GitHub. Results and Contributions: Briefly, TTAG+R presents information about 245 top trending Android Free Games, 97 top trending Android Grossing Games, and 52 top trending Android Paid Games with a total of 8,423 user reviews in 12 different. csv files. The main contributions of this paper are: (i) provides one-place comprehensive data on Android Apps, (ii) describes various features of Android apps and their user reviews, (iii) reports the updated and latest knowledge about Android apps, and (iv) provides the data in an unfiltered form so that researchers may not find difficulty in using this dataset in their data-driven experimentation. From a research implication viewpoint, the dataset supports: (i) understanding the usability characteristics of Android apps, (ii) discovering current trends and pitfalls in Android apps, and (iii) analyzing the Android financial market. Conclusion and Future Work: Thus, TTAG+R is freely available to the research community, and useful for future enhancements in the Android domain. In the future, we plan to keep the data up-to-date with the most recent information for the continued usage of the dataset.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129612253","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 : 2022-12-01DOI: 10.1109/QRS-C57518.2022.00081
Qi Yao, Yuqian Zhou, Yaqi Dong
Quantum random access codes (QRACs) are key tools for a variety of protocols in quantum information theory. This paper gives an upper bound on the guessing success probability in the classical case of random access codes using mutually unbiased bases as measurement bases in a 3-dimensional Hilbert space and gives an encoding strategy capable of exceeding the classical bound. This encoding strategy holds for both 3-1 and 4-1 QRACs. This result is useful in areas such as random number expansion.
{"title":"Quantum Random Access Codes with Mutually Unbiased Bases in Three-Dimensional Hilbert Space","authors":"Qi Yao, Yuqian Zhou, Yaqi Dong","doi":"10.1109/QRS-C57518.2022.00081","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00081","url":null,"abstract":"Quantum random access codes (QRACs) are key tools for a variety of protocols in quantum information theory. This paper gives an upper bound on the guessing success probability in the classical case of random access codes using mutually unbiased bases as measurement bases in a 3-dimensional Hilbert space and gives an encoding strategy capable of exceeding the classical bound. This encoding strategy holds for both 3-1 and 4-1 QRACs. This result is useful in areas such as random number expansion.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127042989","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 : 2022-12-01DOI: 10.1109/QRS-C57518.2022.00109
Chang Xu, Heng Ding, Xuejian Zhang, Cong Wang, Hongji Yang
The computer game is the Drosophila in the field of artificial intelligence. Recently, a series of computer game systems., such as AlphaGo and AlphaGo Zero, defeating the world human champion of Go, has greatly refreshed people's understanding of the creativity of machine. This paper applies the deep reinforcement learning method to the computer Chinese Chess. We are committed to decrease the demand for computing resources heavily from multi-perspectives, such as data augmentation and using more intermediate results as labels. The experiment shows that the level of our program is increased rapidly.
{"title":"A Data-Efficient Method of Deep Reinforcement Learning for Chinese Chess","authors":"Chang Xu, Heng Ding, Xuejian Zhang, Cong Wang, Hongji Yang","doi":"10.1109/QRS-C57518.2022.00109","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00109","url":null,"abstract":"The computer game is the Drosophila in the field of artificial intelligence. Recently, a series of computer game systems., such as AlphaGo and AlphaGo Zero, defeating the world human champion of Go, has greatly refreshed people's understanding of the creativity of machine. This paper applies the deep reinforcement learning method to the computer Chinese Chess. We are committed to decrease the demand for computing resources heavily from multi-perspectives, such as data augmentation and using more intermediate results as labels. The experiment shows that the level of our program is increased rapidly.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130585157","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 : 2022-12-01DOI: 10.1109/QRS-C57518.2022.00068
Yunhao Li, Shijie Chen, Jiancheng Zhao
Classic recommendation technologies such as collaborative filtering have some challenging problems such as cold start. Because knowledge graph can enrich data information, in recent years, many scholars have applied it to recommendation systems to solve the above problems. However, Most of the methods only exploit relations and entities involved in the knowledge graph, and do not further explore the correlation information between the entities in the knowledge graph. In order to solve the above problems, recommendation algorithm for graph convolutional networks based on multi-relational knowledge graph (Multi-RKGCN) is proposed, which expands the relations and entities in knowledge graph through reflexive and self-circulating ways. At the time of aggregation, the tail entity and the corresponding relationship are combined and embedded by the knowledge graph embedding technology to enrich the semantics of the entity. Finally, the performance of AUC and F1 is verified on two publicly available datasets. The experimental results show that Multi-RKGCN method is better than KGCN method.
{"title":"Recommendation Algorithm for Graph Convolutional Networks based on Multi-Ralational Knowledge Graph","authors":"Yunhao Li, Shijie Chen, Jiancheng Zhao","doi":"10.1109/QRS-C57518.2022.00068","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00068","url":null,"abstract":"Classic recommendation technologies such as collaborative filtering have some challenging problems such as cold start. Because knowledge graph can enrich data information, in recent years, many scholars have applied it to recommendation systems to solve the above problems. However, Most of the methods only exploit relations and entities involved in the knowledge graph, and do not further explore the correlation information between the entities in the knowledge graph. In order to solve the above problems, recommendation algorithm for graph convolutional networks based on multi-relational knowledge graph (Multi-RKGCN) is proposed, which expands the relations and entities in knowledge graph through reflexive and self-circulating ways. At the time of aggregation, the tail entity and the corresponding relationship are combined and embedded by the knowledge graph embedding technology to enrich the semantics of the entity. Finally, the performance of AUC and F1 is verified on two publicly available datasets. The experimental results show that Multi-RKGCN method is better than KGCN method.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125842937","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 : 2022-12-01DOI: 10.1109/QRS-C57518.2022.00057
Yue Ma, Ya Pan, Yong Fan
The existing studies have applied metamorphic testing technique to testing the medical image classification models, effectively alleviating the test oracle problem and reducing the testing difficulty. However, existing methods mainly focus on constructing metamorphic relations by using general image transformation methods, without combining the knowledge characteristics of medical imaging domain, resulting in problems such as low validity of metamorphic relations. According to the above problems, this paper based on the premise of conforming to the real scenario of image diagnosis, combining the key information of medical image semantics, and constructing general metamorphic relations in this field from three dimensions: the characteristics of medical images in real environment, the regular changes of lesion stage in images and the motion artifacts produced by patients in the process of filming. The medical images classification models of COVID-19 were also selected for instance validation, and the metamorphic relations were quantitatively analyzed to detect inconsistency in the classification results of different models and to assess the robustness of the model. The experimental results show that the constructed metamorphic relations by the key information of medical image semantics are able to detect inconsistencies in the models with a high detection capability, with the inconsistency percentage reaching up to 38.05%. This method can also be extended to test different types of medical image classification models.
{"title":"Metamorphic Testing for the Medical Image Classification Model","authors":"Yue Ma, Ya Pan, Yong Fan","doi":"10.1109/QRS-C57518.2022.00057","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00057","url":null,"abstract":"The existing studies have applied metamorphic testing technique to testing the medical image classification models, effectively alleviating the test oracle problem and reducing the testing difficulty. However, existing methods mainly focus on constructing metamorphic relations by using general image transformation methods, without combining the knowledge characteristics of medical imaging domain, resulting in problems such as low validity of metamorphic relations. According to the above problems, this paper based on the premise of conforming to the real scenario of image diagnosis, combining the key information of medical image semantics, and constructing general metamorphic relations in this field from three dimensions: the characteristics of medical images in real environment, the regular changes of lesion stage in images and the motion artifacts produced by patients in the process of filming. The medical images classification models of COVID-19 were also selected for instance validation, and the metamorphic relations were quantitatively analyzed to detect inconsistency in the classification results of different models and to assess the robustness of the model. The experimental results show that the constructed metamorphic relations by the key information of medical image semantics are able to detect inconsistencies in the models with a high detection capability, with the inconsistency percentage reaching up to 38.05%. This method can also be extended to test different types of medical image classification models.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128175515","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 : 2022-12-01DOI: 10.1109/QRS-C57518.2022.00015
Ryohei Sato, Hidetoshi Kawaguchi, Yuichi Nakatani
To efficiently manage security risks of network systems, vulnerabilities in the systems need to be assessed to determine their severity or priority. The Bayesian attack graph (BAG) is a risk analysis model that takes into account the probabilities of vulnerability exploitation (exploit probabilities) and their dependencies to calculate the probabilities that specific assets are compromised (compromise probabilities) in a system. In many BAG analysis methods, an exploit probability is obtained assuming that it strongly correlates with base metrics of the Common Vulnerability Scoring System (CVSS) assigned to the corresponding vulnerability. However, the authors found that this assumption does not necessarily hold, and thus the accuracy of compromise probabilities estimated by these methods may be impaired. Therefore, this paper proposes the exploit time probability (ETP)-model to calculate well-founded exploit probabilities on the basis of empirical data on vulnerabilities and exploits. The model uses Weibull distributions to approximate the probability distribution of the time between the publication of a vulnerability to the National Vulnerability Database (NVD) and its exploitation. Finally, by applying the ETP-model to a test network, the model is shown to be able to provide reasonable exploit probabilities and be a fundamental technique to improve the accuracy of existing BAG analysis methods.
{"title":"A Stochastic Model for Calculating Well-Founded Probabilities of Vulnerability Exploitation","authors":"Ryohei Sato, Hidetoshi Kawaguchi, Yuichi Nakatani","doi":"10.1109/QRS-C57518.2022.00015","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00015","url":null,"abstract":"To efficiently manage security risks of network systems, vulnerabilities in the systems need to be assessed to determine their severity or priority. The Bayesian attack graph (BAG) is a risk analysis model that takes into account the probabilities of vulnerability exploitation (exploit probabilities) and their dependencies to calculate the probabilities that specific assets are compromised (compromise probabilities) in a system. In many BAG analysis methods, an exploit probability is obtained assuming that it strongly correlates with base metrics of the Common Vulnerability Scoring System (CVSS) assigned to the corresponding vulnerability. However, the authors found that this assumption does not necessarily hold, and thus the accuracy of compromise probabilities estimated by these methods may be impaired. Therefore, this paper proposes the exploit time probability (ETP)-model to calculate well-founded exploit probabilities on the basis of empirical data on vulnerabilities and exploits. The model uses Weibull distributions to approximate the probability distribution of the time between the publication of a vulnerability to the National Vulnerability Database (NVD) and its exploitation. Finally, by applying the ETP-model to a test network, the model is shown to be able to provide reasonable exploit probabilities and be a fundamental technique to improve the accuracy of existing BAG analysis methods.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"319 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115836661","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 : 2022-12-01DOI: 10.1109/QRS-C57518.2022.00086
Yahan Yu, Yixuan Xu, Jian Wang, Zhenxing Li, Bin Cao
The contemporary financial industry is a highly information-based industry. The digital system can establish a complete information system around various attributes and behaviors of bank accounts. In the core business system, most of this information is constantly changing and recorded in real time. Therefore, we can achieve the goal of monitoring the money laundering risk of the account by analyzing the relevant element data and specific characteristics of the account. The risk assessment and customer classification indicator system for accounts is composed of four basic elements: customer characteristics, location, business development and industry conditions. Account money laundering risk indicators are composed of various basic elements and their risk sub-items. We propose an aspect-based (aspect-level) graph convolutional neural network, starting from different perspectives, to quantify the risk of money laundering in financial institutions.
{"title":"Anti-Money Laundering Risk Identification of Financial Institutions based on Aspect-Level Graph Neural Networks","authors":"Yahan Yu, Yixuan Xu, Jian Wang, Zhenxing Li, Bin Cao","doi":"10.1109/QRS-C57518.2022.00086","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00086","url":null,"abstract":"The contemporary financial industry is a highly information-based industry. The digital system can establish a complete information system around various attributes and behaviors of bank accounts. In the core business system, most of this information is constantly changing and recorded in real time. Therefore, we can achieve the goal of monitoring the money laundering risk of the account by analyzing the relevant element data and specific characteristics of the account. The risk assessment and customer classification indicator system for accounts is composed of four basic elements: customer characteristics, location, business development and industry conditions. Account money laundering risk indicators are composed of various basic elements and their risk sub-items. We propose an aspect-based (aspect-level) graph convolutional neural network, starting from different perspectives, to quantify the risk of money laundering in financial institutions.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133450554","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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