Cloud computing is a significant leap in the development process of the information industry. It integrates computing, storage, network, and other information resources organically, providing more convenient means for deep sharing and utilization, making the acquisition of information resources no longer limited by time and space. Cloud computing platform has become one of the important components of Information infrastructure, providing an important computing foundation for promoting the Digital transformation of the industry. As cloud computing platforms are increasingly accepted, more and more enterprises are migrating applications and Data migration to the cloud to effectively break through the limitations of local computing and storage resources, and make better use of the development trend of the cloud’s powerful analysis and processing capabilities. Therefore, to ensure the security and credibility of cloud platform Data migration, this paper proposes a key allocation method based on the hash function to support the trusted migration of cloud platform data. Experimental results have shown that the cloud platform key allocation scheme proposed in this article can effectively improve the security capabilities of the cloud platform compared to traditional key allocation schemes.
{"title":"Research on key distribution methods supporting trusted data migration of cloud platform","authors":"Fuqiang Tian, Jun Mou, Angang Liu, Zhongkui Zhu, Maonan Lin, Hongyu Wu","doi":"10.1117/12.3032077","DOIUrl":"https://doi.org/10.1117/12.3032077","url":null,"abstract":"Cloud computing is a significant leap in the development process of the information industry. It integrates computing, storage, network, and other information resources organically, providing more convenient means for deep sharing and utilization, making the acquisition of information resources no longer limited by time and space. Cloud computing platform has become one of the important components of Information infrastructure, providing an important computing foundation for promoting the Digital transformation of the industry. As cloud computing platforms are increasingly accepted, more and more enterprises are migrating applications and Data migration to the cloud to effectively break through the limitations of local computing and storage resources, and make better use of the development trend of the cloud’s powerful analysis and processing capabilities. Therefore, to ensure the security and credibility of cloud platform Data migration, this paper proposes a key allocation method based on the hash function to support the trusted migration of cloud platform data. Experimental results have shown that the cloud platform key allocation scheme proposed in this article can effectively improve the security capabilities of the cloud platform compared to traditional key allocation schemes.","PeriodicalId":198425,"journal":{"name":"Other Conferences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141378363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In modern e-healthcare systems, healthcare providers usually store users' data in cloud servers. Users wish to obtain relevant diagnostic files through data generated by body sensors. We propose an efficient and privacy-preserving Top- k disease matching scheme (called EPTDMS). EPTDMS uses Density-Sensitive Hashing (DSH) to implement fuzzy search in stage one, employs the cosine value to sort the relevant result, and obtains patient diagnostic files. Improvements are made to address the problems of low matching efficiency, high computational overhead, and high communication volume of most privacy-preserving matching schemes. This scheme achieves disease matching with low computation and communication overhead and reduces the average query time.
在现代电子医疗系统中，医疗服务提供商通常将用户数据存储在云服务器中。用户希望通过身体传感器生成的数据获得相关诊断文件。我们提出了一种高效且保护隐私的 Top- k 疾病匹配方案（称为 EPTDMS）。EPTDMS 在第一阶段使用密度敏感散列（DSH）实现模糊搜索，利用余弦值对相关结果进行排序，并获取患者诊断文件。针对大多数隐私保护匹配方案存在的匹配效率低、计算开销大、通信量大等问题进行了改进。该方案以较低的计算和通信开销实现了疾病匹配，并缩短了平均查询时间。
{"title":"EPTDMS: efficient and privacy-preserving top-k disease matching scheme for cloud-assisted e-healthcare system","authors":"ou ruan, xin jiang","doi":"10.1117/12.3031898","DOIUrl":"https://doi.org/10.1117/12.3031898","url":null,"abstract":"In modern e-healthcare systems, healthcare providers usually store users' data in cloud servers. Users wish to obtain relevant diagnostic files through data generated by body sensors. We propose an efficient and privacy-preserving Top- k disease matching scheme (called EPTDMS). EPTDMS uses Density-Sensitive Hashing (DSH) to implement fuzzy search in stage one, employs the cosine value to sort the relevant result, and obtains patient diagnostic files. Improvements are made to address the problems of low matching efficiency, high computational overhead, and high communication volume of most privacy-preserving matching schemes. This scheme achieves disease matching with low computation and communication overhead and reduces the average query time.","PeriodicalId":198425,"journal":{"name":"Other Conferences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141379505","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}
To enhance the fire service system, many countries have conducted extensive research on “Smart Firefighting” platform architecture and achieved significant results. However, there are still numerous challenges that persist in the field. To address the challenges related to storage, computing, security, and scalability of large-scale data in the "Smart Firefighting" domain, we have conducted an in-depth study and comparison of existing "Smart Firefighting" platform architectures. Based on the analysis, we propose a novel architectural for the "Smart Firefighting" platform, which combines the strengths of traditional hierarchical architecture with the Cloud-Edge-End architectural, this integration effectively preserves the key features of both architectural styles, and the proposed fusion model is able to satisfy the computation and storage of large amounts of firefighting data while guaranteeing security. This architectural model can serve as a reference for the construction of highly available, high-performance, and more secure and stable "Smart Firefighting" platforms and holds significant importance in accelerating the development and maintenance of "Smart Firefighting" to ensure long-term social stability and security.
{"title":"A hierarchical software architecture for smart firefighting platform","authors":"Zixiang Zhang, Nady Slam, Zhengqiang Di, Yu Zhu","doi":"10.1117/12.3032097","DOIUrl":"https://doi.org/10.1117/12.3032097","url":null,"abstract":"To enhance the fire service system, many countries have conducted extensive research on “Smart Firefighting” platform architecture and achieved significant results. However, there are still numerous challenges that persist in the field. To address the challenges related to storage, computing, security, and scalability of large-scale data in the \"Smart Firefighting\" domain, we have conducted an in-depth study and comparison of existing \"Smart Firefighting\" platform architectures. Based on the analysis, we propose a novel architectural for the \"Smart Firefighting\" platform, which combines the strengths of traditional hierarchical architecture with the Cloud-Edge-End architectural, this integration effectively preserves the key features of both architectural styles, and the proposed fusion model is able to satisfy the computation and storage of large amounts of firefighting data while guaranteeing security. This architectural model can serve as a reference for the construction of highly available, high-performance, and more secure and stable \"Smart Firefighting\" platforms and holds significant importance in accelerating the development and maintenance of \"Smart Firefighting\" to ensure long-term social stability and security.","PeriodicalId":198425,"journal":{"name":"Other Conferences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141379950","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}
At the present stage, just-time software defect prediction has garnered significant attention from researchers due to its granularity and immediacy. Primarily utilizing machine learning classifiers, these models are trained on information from code repositories to predict whether future changes may lead to defects. However, a current challenge with these classifiers lies in the vast number of features, leading to decreased prediction efficiency. These features not only impact model performance but can sometimes result in a decline in predictive accuracy. This paper explores a feature selection technique that combines random forests and self-attention to discard less important features without compromising performance. Through this approach, the number of features required for training is significantly reduced, often to less than 50% of the original features. In our study across six software projects, we observed that using feature selection in the KNN model led to a 9% improvement in the F1 metric and a 6% improvement in the AUC metric compared to logistic regression and Bayesian models. Finally, we applied SHAP for interpretability analysis of the model. This research contributes to enhancing the accuracy and efficiency of just-in-time software defect prediction, providing valuable insights for research and practice in related fields.
{"title":"Just-in-time software defect prediction based on feature selection","authors":"Shipeng cai, Hongmin Ren","doi":"10.1117/12.3031976","DOIUrl":"https://doi.org/10.1117/12.3031976","url":null,"abstract":"At the present stage, just-time software defect prediction has garnered significant attention from researchers due to its granularity and immediacy. Primarily utilizing machine learning classifiers, these models are trained on information from code repositories to predict whether future changes may lead to defects. However, a current challenge with these classifiers lies in the vast number of features, leading to decreased prediction efficiency. These features not only impact model performance but can sometimes result in a decline in predictive accuracy. This paper explores a feature selection technique that combines random forests and self-attention to discard less important features without compromising performance. Through this approach, the number of features required for training is significantly reduced, often to less than 50% of the original features. In our study across six software projects, we observed that using feature selection in the KNN model led to a 9% improvement in the F1 metric and a 6% improvement in the AUC metric compared to logistic regression and Bayesian models. Finally, we applied SHAP for interpretability analysis of the model. This research contributes to enhancing the accuracy and efficiency of just-in-time software defect prediction, providing valuable insights for research and practice in related fields.","PeriodicalId":198425,"journal":{"name":"Other Conferences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141380831","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}
Abstract: In the field of signal processing, array signal processing is an important part. As an important research direction in the field of array signal processing, beamforming technology has great significance in many fields. In this paper, based on the advantages of FPGA technology, the beam control and adaptive algorithm are studied and the design scheme is proposed. Finally, the design is verified by simulation on ModelSim and Matlab platforms. The experiment proves that the error of this scheme is small and the expected convergence effect can be achieved.
{"title":"Research on adaptive beamforming algorithm based on FPGA","authors":"Youbang Su","doi":"10.1117/12.3032050","DOIUrl":"https://doi.org/10.1117/12.3032050","url":null,"abstract":"Abstract: In the field of signal processing, array signal processing is an important part. As an important research direction in the field of array signal processing, beamforming technology has great significance in many fields. In this paper, based on the advantages of FPGA technology, the beam control and adaptive algorithm are studied and the design scheme is proposed. Finally, the design is verified by simulation on ModelSim and Matlab platforms. The experiment proves that the error of this scheme is small and the expected convergence effect can be achieved.","PeriodicalId":198425,"journal":{"name":"Other Conferences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141378116","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}
Deep neural networks excel in remote sensing image semantic segmentation, but existing methods, despite their sophistication, often focus on channel and spatial dependencies within identical feature maps. This can lead to a uniform treatment of diverse feature maps, hindering information exchange and impacting model efficacy. To address this, we introduce Feature Map Attention, dynamically modulating weights based on interdependencies among various feature maps. This fosters connections and feature fusion, enhancing the model's capability to represent features. Importantly, this improvement comes with minimal additional computational expense. We also incorporate multipath skip connections, efficiently transmitting features at various scales from encoder to decoder, boosting overall model effectiveness. Our FMAMPN, a lightweight neural network, outperforms other state-of-the-art lightweight models across various datasets.
{"title":"FMAMPN: lightweight feature map attention multipath network for semantic segmentation of remote sensing image","authors":"Songqi Hou, Ying Yuan","doi":"10.1117/12.3031941","DOIUrl":"https://doi.org/10.1117/12.3031941","url":null,"abstract":"Deep neural networks excel in remote sensing image semantic segmentation, but existing methods, despite their sophistication, often focus on channel and spatial dependencies within identical feature maps. This can lead to a uniform treatment of diverse feature maps, hindering information exchange and impacting model efficacy. To address this, we introduce Feature Map Attention, dynamically modulating weights based on interdependencies among various feature maps. This fosters connections and feature fusion, enhancing the model's capability to represent features. Importantly, this improvement comes with minimal additional computational expense. We also incorporate multipath skip connections, efficiently transmitting features at various scales from encoder to decoder, boosting overall model effectiveness. Our FMAMPN, a lightweight neural network, outperforms other state-of-the-art lightweight models across various datasets.","PeriodicalId":198425,"journal":{"name":"Other Conferences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141378099","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}
We found that there are existing academic studies on reverse engineering the firmware that implements Bluetooth and USB protocols to study their security. Electronic Control Unit (ECU) firmware implements the Controller Area Network (CAN) protocol, which is commonly used to implement the communication of the vehicle’s internal network. With the development and growth of electric vehicles, the security of the vehicle network is becoming increasingly important. This paper proposes a method to reverse engineer ECU firmware, which can efficiently help us quickly identify the library functions in the firmware and reduce the errors that may occur during reverse engineering.
我们发现，目前已有学术研究对执行蓝牙和 USB 协议的固件进行逆向工程，以研究其安全性。电子控制单元（ECU）固件实现了控制器局域网（CAN）协议，该协议通常用于实现汽车内部网络的通信。随着电动汽车的发展和壮大，汽车网络的安全性变得越来越重要。本文提出了一种对 ECU 固件进行逆向工程的方法，可以有效地帮助我们快速识别固件中的库函数，减少逆向工程中可能出现的错误。
{"title":"An improved method for reverse engineering ECU firmware","authors":"Yuhao Qiu","doi":"10.1117/12.3032054","DOIUrl":"https://doi.org/10.1117/12.3032054","url":null,"abstract":"We found that there are existing academic studies on reverse engineering the firmware that implements Bluetooth and USB protocols to study their security. Electronic Control Unit (ECU) firmware implements the Controller Area Network (CAN) protocol, which is commonly used to implement the communication of the vehicle’s internal network. With the development and growth of electric vehicles, the security of the vehicle network is becoming increasingly important. This paper proposes a method to reverse engineer ECU firmware, which can efficiently help us quickly identify the library functions in the firmware and reduce the errors that may occur during reverse engineering.","PeriodicalId":198425,"journal":{"name":"Other Conferences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141379795","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}
Lei Wang, Li Sun, Duo Shang, Huihong He, Guangtao Nie, Haikuo Dong
The CVE is a database of cyber security vulnerabilities that describe the characteristics of vulnerabilities. MITRE ATT&CK formalizes attack patterns (including attack tactics and techniques) through abstract theory, and gives corresponding mitigation schemes, so it is significant to identify ATT&CK attack information from CVE. Since there is no link between the two, this paper proposes a Transformer-based mapping scheme to automatically map CVE’s to ATT&CK, and verify it on the CVE dataset, which has a good mapping effect and provides security operators with Intuitive reference.
{"title":"Automatic mapping based on CVE and ATT&CK","authors":"Lei Wang, Li Sun, Duo Shang, Huihong He, Guangtao Nie, Haikuo Dong","doi":"10.1117/12.3032103","DOIUrl":"https://doi.org/10.1117/12.3032103","url":null,"abstract":"The CVE is a database of cyber security vulnerabilities that describe the characteristics of vulnerabilities. MITRE ATT&CK formalizes attack patterns (including attack tactics and techniques) through abstract theory, and gives corresponding mitigation schemes, so it is significant to identify ATT&CK attack information from CVE. Since there is no link between the two, this paper proposes a Transformer-based mapping scheme to automatically map CVE’s to ATT&CK, and verify it on the CVE dataset, which has a good mapping effect and provides security operators with Intuitive reference.","PeriodicalId":198425,"journal":{"name":"Other Conferences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141380797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the popularity of microservices architecture, how to refactor a monolithic system into a microservices architecture has become a challenge. Traditional system refactoring strategies often focus on a single aspect, such as conducting source code analysis or system load analysis in isolation. This approach has a limited perspective and cannot comprehensively integrate the diverse characteristics of the system for refactoring. This paper proposes a microservices partitioning method for monolithic systems that integrates multiple features. Firstly, it integrates source code and runtime system data to comprehensively acquire system characteristics. By calculating the semantic similarity of class documents, the similarity of abstract syntax trees, and the frequency of interaction between classes, three types of weights are generated. Then, by assigning different weights, a comprehensive weight is calculated to construct an undirected weighted graph representing dependency relationships. Finally, the Chinese Whisper clustering algorithm is used to partition monolithic systems, obtaining suitable microservice modules. Experimental results show that this method can help developers better understand and partition the system, achieving a microservices architecture with high cohesion and low coupling.
{"title":"A method for microservice partitioning of monolithic systems based on multifeature fusion","authors":"Xiaoyan Gao, Junfeng Zhao","doi":"10.1117/12.3031975","DOIUrl":"https://doi.org/10.1117/12.3031975","url":null,"abstract":"With the popularity of microservices architecture, how to refactor a monolithic system into a microservices architecture has become a challenge. Traditional system refactoring strategies often focus on a single aspect, such as conducting source code analysis or system load analysis in isolation. This approach has a limited perspective and cannot comprehensively integrate the diverse characteristics of the system for refactoring. This paper proposes a microservices partitioning method for monolithic systems that integrates multiple features. Firstly, it integrates source code and runtime system data to comprehensively acquire system characteristics. By calculating the semantic similarity of class documents, the similarity of abstract syntax trees, and the frequency of interaction between classes, three types of weights are generated. Then, by assigning different weights, a comprehensive weight is calculated to construct an undirected weighted graph representing dependency relationships. Finally, the Chinese Whisper clustering algorithm is used to partition monolithic systems, obtaining suitable microservice modules. Experimental results show that this method can help developers better understand and partition the system, achieving a microservices architecture with high cohesion and low coupling.","PeriodicalId":198425,"journal":{"name":"Other Conferences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141375973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents the need for innovative solutions to optimize computational power networks and the application of quantum computing to tackle real-world challenges. Our research addresses this critical issue by developing a robust pre-training scheme that integrates Quantum Unweighted Quadratic Unconstrained Binary Optimization (QUBO) models with quantum-inspired algorithms. This approach aims to enhance the adversarial robustness of CVQKD systems, ensuring their security in the face of sophisticated hacking attempts. Our experimental results demonstrate that the proposed strategy effectively defends against adversarial attacks while maintaining the integrity of secret keys, showcasing the adaptability and efficiency of QUBO models in quantum communication scenarios. This work not only contributes to the broader application of QUBO models in quantum communication but also provides a robust pre-training scheme that can be generalized and transplanted to other machine learning-assisted systems, significantly improving their security in the face of adversarial attacks.
{"title":"Based on QUBO models with quantum-inspired algorithms to enhance the CVQKD systems to ensure security of hacking","authors":"Feiyue Zhu, Haifeng Qiu, Ziyu Wang","doi":"10.1117/12.3031949","DOIUrl":"https://doi.org/10.1117/12.3031949","url":null,"abstract":"This paper presents the need for innovative solutions to optimize computational power networks and the application of quantum computing to tackle real-world challenges. Our research addresses this critical issue by developing a robust pre-training scheme that integrates Quantum Unweighted Quadratic Unconstrained Binary Optimization (QUBO) models with quantum-inspired algorithms. This approach aims to enhance the adversarial robustness of CVQKD systems, ensuring their security in the face of sophisticated hacking attempts. Our experimental results demonstrate that the proposed strategy effectively defends against adversarial attacks while maintaining the integrity of secret keys, showcasing the adaptability and efficiency of QUBO models in quantum communication scenarios. This work not only contributes to the broader application of QUBO models in quantum communication but also provides a robust pre-training scheme that can be generalized and transplanted to other machine learning-assisted systems, significantly improving their security in the face of adversarial attacks.","PeriodicalId":198425,"journal":{"name":"Other Conferences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141377141","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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