Dating application use is on the rise, and with it comes the need to better understand what data can be recovered to assist in an investigation. While using these dating applications, people send countless messages (including pictures and videos) without ever considering exactly what data is being sent within that message. In this project, we conduct a forensic analysis of five popular dating applications (Her, Hily, Hinge, OkCupid, and Plenty of Fish (POF)) that are available on both Android and iOS devices. We also determined what forensically relevant data can be recovered from dating applications on both Android and iOS. Specifically, we determined what data can be recovered about the sender from the receiver's phone. Secondly, we identified any privacy concerns that result due to the recoverable data and discuss their implications for users. Lastly, we detailed the investigative process that should be followed and presented the locations of any relevant data to aid digital forensics investigators during an Investigation.
约会应用程序的使用正在上升，随之而来的是需要更好地了解可以恢复哪些数据以协助调查。在使用这些约会应用程序时，人们发送了无数的信息(包括图片和视频)，却从来没有考虑过这些信息中到底发送了什么数据。在这个项目中，我们对五个流行的约会应用程序(Her, Hily, Hinge, OkCupid和Plenty of Fish (POF))进行了取证分析，这些应用程序可以在Android和iOS设备上使用。我们还确定了可以从Android和iOS上的约会应用程序中恢复哪些法医相关数据。具体来说，我们确定了可以从接收者的手机中恢复哪些关于发送者的数据。其次，我们确定了由于可恢复数据而导致的任何隐私问题，并讨论了它们对用户的影响。最后，我们详细介绍了应遵循的调查过程，并提供了任何相关数据的位置，以帮助调查期间的数字取证调查员。
{"title":"Forensic Analysis of Dating Applications on Android and iOS Devices","authors":"Shinelle Hutchinson, Neesha Shantaram, Umit Karabiyik","doi":"10.1109/TrustCom50675.2020.00113","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00113","url":null,"abstract":"Dating application use is on the rise, and with it comes the need to better understand what data can be recovered to assist in an investigation. While using these dating applications, people send countless messages (including pictures and videos) without ever considering exactly what data is being sent within that message. In this project, we conduct a forensic analysis of five popular dating applications (Her, Hily, Hinge, OkCupid, and Plenty of Fish (POF)) that are available on both Android and iOS devices. We also determined what forensically relevant data can be recovered from dating applications on both Android and iOS. Specifically, we determined what data can be recovered about the sender from the receiver's phone. Secondly, we identified any privacy concerns that result due to the recoverable data and discuss their implications for users. Lastly, we detailed the investigative process that should be followed and presented the locations of any relevant data to aid digital forensics investigators during an Investigation.","PeriodicalId":221956,"journal":{"name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128974562","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 : 2020-12-01DOI: 10.1109/TrustCom50675.2020.00213
Xiaoying Qiu, Guangda Li, Xuan Sun, Zhiguo Du
How to improve the security and stability of wireless communication systems has become a critical issue. In this paper, physical layer security is introduced to overcome security challenges. The considered communication system is equipped with full-duplex (FD) users in contrast to conventional frameworks where half-duplex (HD) users are at hand. Under these assumptions, we propose a Q-learning based user selection algorithm to model the interaction between a source and multiple users. We also investigate the effect of self-interference and channel interference on physical layer security. The numerical results verify the superiority of the proposed algorithm and in certain conditions, demonstrate substantial performance gain over the conventional approaches.
{"title":"Exploiting User Selection Algorithm for Securing Wireless Communication Networks","authors":"Xiaoying Qiu, Guangda Li, Xuan Sun, Zhiguo Du","doi":"10.1109/TrustCom50675.2020.00213","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00213","url":null,"abstract":"How to improve the security and stability of wireless communication systems has become a critical issue. In this paper, physical layer security is introduced to overcome security challenges. The considered communication system is equipped with full-duplex (FD) users in contrast to conventional frameworks where half-duplex (HD) users are at hand. Under these assumptions, we propose a Q-learning based user selection algorithm to model the interaction between a source and multiple users. We also investigate the effect of self-interference and channel interference on physical layer security. The numerical results verify the superiority of the proposed algorithm and in certain conditions, demonstrate substantial performance gain over the conventional approaches.","PeriodicalId":221956,"journal":{"name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129150338","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 : 2020-12-01DOI: 10.1109/TrustCom50675.2020.00028
Yin Liu, E. Tilevich
As the trusted computing base (TCB) unnecessarily increases its size, the performance and security of Trusted Execution Environments (TEE) can deteriorate rapidly. Existing solutions focus on placing only the necessary program parts in TEE, but neglect the numerous cases of legacy software with misplaced TEE-based non-sensitive code. In this paper, we introduce a new type of software refactoring—TEE Insourcing—that identifies and migrates non-sensitive code out of TEE. We present TEE-DRUP, the first semi-automated TEE Insourcing framework whose process comprises two phases: (1) a variable sensitivity analysis designates each variable as sensitive or non-sensitive; (2) a compiler-assisted program transformation automatically moves the functions that never operate on the sensitive variables out of TEE. Developers can participate to verify and confirm sensitive variables, and specify additional non-sensitive functions to migrate. The evaluation results of TEE-DRUP on real-world programs are encouraging. TEE-DRUP distinguishes between sensitive and non-sensitive variables with satisfactory accuracy, precision, and recall — all of their actual values are greater than 80% in the majority of evaluation scenarios. Further, moving non-sensitive code out of TEE improves system performance, with the speedup ranging between 1.35 and 10K. Finally, TEE-DRUP's automated program transformation requires only a small programming effort.
{"title":"Reducing the Price of Protection: Identifying and Migrating Non-Sensitive Code in TEE","authors":"Yin Liu, E. Tilevich","doi":"10.1109/TrustCom50675.2020.00028","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00028","url":null,"abstract":"As the trusted computing base (TCB) unnecessarily increases its size, the performance and security of Trusted Execution Environments (TEE) can deteriorate rapidly. Existing solutions focus on placing only the necessary program parts in TEE, but neglect the numerous cases of legacy software with misplaced TEE-based non-sensitive code. In this paper, we introduce a new type of software refactoring—TEE Insourcing—that identifies and migrates non-sensitive code out of TEE. We present TEE-DRUP, the first semi-automated TEE Insourcing framework whose process comprises two phases: (1) a variable sensitivity analysis designates each variable as sensitive or non-sensitive; (2) a compiler-assisted program transformation automatically moves the functions that never operate on the sensitive variables out of TEE. Developers can participate to verify and confirm sensitive variables, and specify additional non-sensitive functions to migrate. The evaluation results of TEE-DRUP on real-world programs are encouraging. TEE-DRUP distinguishes between sensitive and non-sensitive variables with satisfactory accuracy, precision, and recall — all of their actual values are greater than 80% in the majority of evaluation scenarios. Further, moving non-sensitive code out of TEE improves system performance, with the speedup ranging between 1.35 and 10K. Finally, TEE-DRUP's automated program transformation requires only a small programming effort.","PeriodicalId":221956,"journal":{"name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131084285","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 : 2020-12-01DOI: 10.1109/trustcom50675.2020.00009
{"title":"AEIT 2020 Organizing and Program Committees","authors":"","doi":"10.1109/trustcom50675.2020.00009","DOIUrl":"https://doi.org/10.1109/trustcom50675.2020.00009","url":null,"abstract":"","PeriodicalId":221956,"journal":{"name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125464201","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 : 2020-12-01DOI: 10.1109/TrustCom50675.2020.00039
Yannan Liu, Yabin Lai, Kaizhi Wei, Liang Gu, Zhengzheng Yan
Automatic family labeling for malware is in demand, especially for today's malware scale. While business Anti-Virus engines provide an efficient family labeling method, the raw labels tend to be inconsistent. Prior works mitigate such inconsistency by detecting the aliases and majority voting to obtain the final family label. However, these methods solve the inconsistency in a coarse-grained and vulnerable manner, and the obtained family label is inaccurate sometimes. In this work, we propose NLabel to conduct familial clustering based on AV engines' raw labels. On the one hand, NLabel uses word embedding techniques to capture the similarity among raw labels, transform the inconsistent labels of the same family into similar semantic representations, and mitigate the inconsistency at finer granularity. On the other hand, we propose a hierarchical family clustering method to boost the performance of large-scale data sets. Experimental results show that our method outperforms the SOTA.
{"title":"NLabel: An Accurate Familial Clustering Framework for Large-scale Weakly-labeled Malware","authors":"Yannan Liu, Yabin Lai, Kaizhi Wei, Liang Gu, Zhengzheng Yan","doi":"10.1109/TrustCom50675.2020.00039","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00039","url":null,"abstract":"Automatic family labeling for malware is in demand, especially for today's malware scale. While business Anti-Virus engines provide an efficient family labeling method, the raw labels tend to be inconsistent. Prior works mitigate such inconsistency by detecting the aliases and majority voting to obtain the final family label. However, these methods solve the inconsistency in a coarse-grained and vulnerable manner, and the obtained family label is inaccurate sometimes. In this work, we propose NLabel to conduct familial clustering based on AV engines' raw labels. On the one hand, NLabel uses word embedding techniques to capture the similarity among raw labels, transform the inconsistent labels of the same family into similar semantic representations, and mitigate the inconsistency at finer granularity. On the other hand, we propose a hierarchical family clustering method to boost the performance of large-scale data sets. Experimental results show that our method outperforms the SOTA.","PeriodicalId":221956,"journal":{"name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126233323","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 : 2020-12-01DOI: 10.1109/TrustCom50675.2020.00029
Wentao Xie, Peng Wu
Machine learning models play an important role for decision-making systems in areas such as hiring, insurance, and predictive policing. However, it still remains a challenge to guarantee their trustworthiness. Fairness is one of the most critical properties of these machine learning models, while individual discriminatory cases may break the trustworthiness of these systems severely. In this paper, we present a systematic approach of testing the fairness of a machine learning model, with individual discriminatory inputs generated automatically in an adaptive manner based on the state-of-the-art deep reinforcement learning techniques. Our approach can explore and exploit the input space efficiently, and find more individual discriminatory inputs within less time consumption. Case studies with typical benchmark models demonstrate the effectiveness and efficiency of our approach, compared to the state-of-the-art black-box fairness testing approaches.
{"title":"Fairness Testing of Machine Learning Models Using Deep Reinforcement Learning","authors":"Wentao Xie, Peng Wu","doi":"10.1109/TrustCom50675.2020.00029","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00029","url":null,"abstract":"Machine learning models play an important role for decision-making systems in areas such as hiring, insurance, and predictive policing. However, it still remains a challenge to guarantee their trustworthiness. Fairness is one of the most critical properties of these machine learning models, while individual discriminatory cases may break the trustworthiness of these systems severely. In this paper, we present a systematic approach of testing the fairness of a machine learning model, with individual discriminatory inputs generated automatically in an adaptive manner based on the state-of-the-art deep reinforcement learning techniques. Our approach can explore and exploit the input space efficiently, and find more individual discriminatory inputs within less time consumption. Case studies with typical benchmark models demonstrate the effectiveness and efficiency of our approach, compared to the state-of-the-art black-box fairness testing approaches.","PeriodicalId":221956,"journal":{"name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126302745","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 : 2020-12-01DOI: 10.1109/TrustCom50675.2020.00247
T. Dimitrakos, Tezcan Dilshener, A. Kravtsov, Antonio La Marra, F. Martinelli, Athanasios Rizos, A. Rosetti, A. Saracino
This work describes the architecture and prototype implementation of a novel trust-aware continuous authorization technology that targets consumer Internet of Things (IoT), e.g., Smart Home. Our approach extends previous authorization models in three complementary ways: (1) By incorporating trust-level evaluation formulae as conditions inside authorization rules and policies, while supporting the evaluation of such policies through the fusion of an Attribute-Based Access Control (ABAC) authorization policy engine with a Trust-Level-Evaluation-Engine (TLEE). (2) By introducing contextualized, continuous monitoring and re-evaluation of policies throughout the authorization life-cycle. That is, mutable attributes about subjects, resources and environment as well as trust levels that are continuously monitored while obtaining an authorization, throughout the duration of or after revoking an existing authorization. Whenever change is detected, the corresponding authorization rules, including both access control rules and trust level expressions, are re-evaluated. (3) By minimizing the computational and memory footprint and maximizing concurrency and modular evaluation to improve performance while preserving the continuity of monitoring. Finally we introduce an application of such model in Zero Trust Architecture (ZTA) for consumer IoT.
{"title":"Trust Aware Continuous Authorization for Zero Trust in Consumer Internet of Things","authors":"T. Dimitrakos, Tezcan Dilshener, A. Kravtsov, Antonio La Marra, F. Martinelli, Athanasios Rizos, A. Rosetti, A. Saracino","doi":"10.1109/TrustCom50675.2020.00247","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00247","url":null,"abstract":"This work describes the architecture and prototype implementation of a novel trust-aware continuous authorization technology that targets consumer Internet of Things (IoT), e.g., Smart Home. Our approach extends previous authorization models in three complementary ways: (1) By incorporating trust-level evaluation formulae as conditions inside authorization rules and policies, while supporting the evaluation of such policies through the fusion of an Attribute-Based Access Control (ABAC) authorization policy engine with a Trust-Level-Evaluation-Engine (TLEE). (2) By introducing contextualized, continuous monitoring and re-evaluation of policies throughout the authorization life-cycle. That is, mutable attributes about subjects, resources and environment as well as trust levels that are continuously monitored while obtaining an authorization, throughout the duration of or after revoking an existing authorization. Whenever change is detected, the corresponding authorization rules, including both access control rules and trust level expressions, are re-evaluated. (3) By minimizing the computational and memory footprint and maximizing concurrency and modular evaluation to improve performance while preserving the continuity of monitoring. Finally we introduce an application of such model in Zero Trust Architecture (ZTA) for consumer IoT.","PeriodicalId":221956,"journal":{"name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"180 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116140883","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 : 2020-12-01DOI: 10.1109/TrustCom50675.2020.00133
Kilian Becher, J. Lagodzinski, T. Strufe
Cobalt is a key ingredient of lithium-ion batteries and therefore is crucial for many modern devices. To ensure ethical sourcing, consumers need a way to verify provenance of their cobalt-based products, including the percentage of artisanally mined (ASM) cobalt. Existing frameworks for provenance and supply chain traceability rely on distributed ledgers. Providing public verifiability via permissionless distributed ledgers is trivial. However, offering public verifiability based on confidential production details seems contradictory. Hence, existing frameworks lack public verifiability of ratios between commodities while ensuring confidentiality of supply chain details. We propose a protocol that allows end consumers to verify the percentage of ASM cobalt in their products. Unlike previous solutions, production details are published and processed entirely in encrypted form by employing homomorphic encryption and proxy re-encryption. Thus, it ensures a high level of confidentiality of supply chain data. It has constant consumer-side complexity, making it suitable for mobile devices.
{"title":"Privacy-Preserving Public Verification of Ethical Cobalt Sourcing","authors":"Kilian Becher, J. Lagodzinski, T. Strufe","doi":"10.1109/TrustCom50675.2020.00133","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00133","url":null,"abstract":"Cobalt is a key ingredient of lithium-ion batteries and therefore is crucial for many modern devices. To ensure ethical sourcing, consumers need a way to verify provenance of their cobalt-based products, including the percentage of artisanally mined (ASM) cobalt. Existing frameworks for provenance and supply chain traceability rely on distributed ledgers. Providing public verifiability via permissionless distributed ledgers is trivial. However, offering public verifiability based on confidential production details seems contradictory. Hence, existing frameworks lack public verifiability of ratios between commodities while ensuring confidentiality of supply chain details. We propose a protocol that allows end consumers to verify the percentage of ASM cobalt in their products. Unlike previous solutions, production details are published and processed entirely in encrypted form by employing homomorphic encryption and proxy re-encryption. Thus, it ensures a high level of confidentiality of supply chain data. It has constant consumer-side complexity, making it suitable for mobile devices.","PeriodicalId":221956,"journal":{"name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116655446","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 : 2020-12-01DOI: 10.1109/TrustCom50675.2020.00164
Tong Liu, Xiaochen Yuan
This paper proposes a novel image self-recovery scheme based on authentication feature extraction. The Authentication Feature Extraction method is proposed to calculate the authentication information. The Set Partitioning in Hierarchical Trees encoding algorithm is employed to calculate the recovery information. Moreover, in order to retrieve the damaged information caused by tampering, we propose to map each block into another position and generate the mapped-recovery information accordingly. In this way, a double assurance of recovery information can be provided. Experimental results show the superior performance of the proposed scheme in terms of image self-recovery. Comparison with the state-of-the-art works demonstrate that the proposed scheme shows efficiency in strong capability for image recovery, and effectiveness of attack resistance.
{"title":"Image Self-Recovery Based on Authentication Feature Extraction","authors":"Tong Liu, Xiaochen Yuan","doi":"10.1109/TrustCom50675.2020.00164","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00164","url":null,"abstract":"This paper proposes a novel image self-recovery scheme based on authentication feature extraction. The Authentication Feature Extraction method is proposed to calculate the authentication information. The Set Partitioning in Hierarchical Trees encoding algorithm is employed to calculate the recovery information. Moreover, in order to retrieve the damaged information caused by tampering, we propose to map each block into another position and generate the mapped-recovery information accordingly. In this way, a double assurance of recovery information can be provided. Experimental results show the superior performance of the proposed scheme in terms of image self-recovery. Comparison with the state-of-the-art works demonstrate that the proposed scheme shows efficiency in strong capability for image recovery, and effectiveness of attack resistance.","PeriodicalId":221956,"journal":{"name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116737363","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 : 2020-12-01DOI: 10.1109/TrustCom50675.2020.00206
Yuefei Wang, Zhen Liu, Liucun Zhu, Xiaoyi Li, Huai-bin Wang
Aiming at the problem of the sick limb condition of the exoskeleton rehabilitation robot affects the smoothness and stability of the robot system during rehabilitation training, this paper proposed an impedance control model for the lower limb exoskeleton rehabilitation robot. The model realizes the flexibility of the robot system by adjusting the impedance control parameters in real time. To verify the validity of the model, we used SCONE software to realize forward dynamics simulation of walking gait. The classical PID impedance control system and fuzzy adaptive impedance control system are simulated respectively. The results show that the fuzzy adaptive control system is more effective to adapt to the changes of limb condition for the impedance control system of lower limb exoskeleton rehabilitation robot.
{"title":"An impedance control method of lower limb exoskeleton rehabilitation robot based on predicted forward dynamics","authors":"Yuefei Wang, Zhen Liu, Liucun Zhu, Xiaoyi Li, Huai-bin Wang","doi":"10.1109/TrustCom50675.2020.00206","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00206","url":null,"abstract":"Aiming at the problem of the sick limb condition of the exoskeleton rehabilitation robot affects the smoothness and stability of the robot system during rehabilitation training, this paper proposed an impedance control model for the lower limb exoskeleton rehabilitation robot. The model realizes the flexibility of the robot system by adjusting the impedance control parameters in real time. To verify the validity of the model, we used SCONE software to realize forward dynamics simulation of walking gait. The classical PID impedance control system and fuzzy adaptive impedance control system are simulated respectively. The results show that the fuzzy adaptive control system is more effective to adapt to the changes of limb condition for the impedance control system of lower limb exoskeleton rehabilitation robot.","PeriodicalId":221956,"journal":{"name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121874312","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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