Pub Date : 2023-11-01DOI: 10.1109/tdsc.2023.3235951
Siwon Huh, Myungkyu Shim, Jihwan Lee, Simon S. Woo, Hyoungshick Kim, Hojoon Lee
Decentralized Identity (DID) is emerging as a new digital identity management scheme that promises users complete control of their personal data and identification without central authority involvement. The World Wide Web Consortium (W3C) has drafted the DID standard and provided reference implementations. We conduct a security analysis of the W3C DID standard and the reference universal resolver implementation, focusing on user privacy in the DID resolving process. The universal resolver is the key component in the architecture that processes DID requests and DID document retrievals. Our analysis demonstrates that privacy issues can arise due to the imprudent design of the universal resolver. Furthermore, we found that side-channels in the DID document caching schemes of real-world DID services can entail privacy concerns. Motivated by our security analysis, we present a novel DID resolving design, called Oblivira, to enable obliviously DID resolving. Oblivira is a secure resolving agent with a small footprint that enforces the universal resolver to resolve requests without knowing their content. We also propose a privacy-preserving DID document caching scheme that eliminates side-channels. Our evaluation results show that Oblivira only incurs approximately 2.6% of overhead on average with different resolver settings (3, 6, and 12 threads).
{"title":"DID We Miss Anything?: Towards Privacy-Preserving Decentralized ID Architecture","authors":"Siwon Huh, Myungkyu Shim, Jihwan Lee, Simon S. Woo, Hyoungshick Kim, Hojoon Lee","doi":"10.1109/tdsc.2023.3235951","DOIUrl":"https://doi.org/10.1109/tdsc.2023.3235951","url":null,"abstract":"Decentralized Identity (DID) is emerging as a new digital identity management scheme that promises users complete control of their personal data and identification without central authority involvement. The World Wide Web Consortium (W3C) has drafted the DID standard and provided reference implementations. We conduct a security analysis of the W3C DID standard and the reference universal resolver implementation, focusing on user privacy in the DID resolving process. The universal resolver is the key component in the architecture that processes DID requests and DID document retrievals. Our analysis demonstrates that privacy issues can arise due to the imprudent design of the universal resolver. Furthermore, we found that side-channels in the DID document caching schemes of real-world DID services can entail privacy concerns. Motivated by our security analysis, we present a novel DID resolving design, called Oblivira, to enable obliviously DID resolving. Oblivira is a secure resolving agent with a small footprint that enforces the universal resolver to resolve requests without knowing their content. We also propose a privacy-preserving DID document caching scheme that eliminates side-channels. Our evaluation results show that Oblivira only incurs approximately 2.6% of overhead on average with different resolver settings (3, 6, and 12 threads).","PeriodicalId":13047,"journal":{"name":"IEEE Transactions on Dependable and Secure Computing","volume":null,"pages":null},"PeriodicalIF":7.3,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62409905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.1109/tdsc.2023.3243907
Hansong Du, Jiu-fen Liu, X. Luo, Yi Zhang
As the ultimate goal of steganalysis, secret message extraction plays a decisive role in obtaining secret communication evidence and cracking down on criminal activities. For STC (Syndrome-Trellis Codes)-based adaptive steganography, existing pioneering work on secret message extraction: the method based on run test under plaintext embedding may misjudge incorrect stego key as correct stego key, resulting in the failure of extraction. To avoid such a situation, this manuscript proposed a secret message extraction method based on optimal hypothesis test with 100% accuracy under plaintext embedding. First, it is proved that there is a probability distribution difference between the sub-sequence extracted by correct and incorrect stego key. Then, based on the difference, an optimal hypothesis test model is designed to recover the correct stego key. Finally, given the probability of type I and II errors, the sample size and threshold in the hypothesis test are derived. Classic adaptive steganography such as HUGO (Highly Undetectable Steganography) and J-UNIWARD (JPEG Universal Wavelet Relative Distortion) have been conducted experiment, showing that the proposed method can extract message with 100% accuracy and 44 bits sample size, which verifies the correctness of the theorem and the effectiveness of the method.
{"title":"Extraction Method of Secret Message Based on Optimal Hypothesis Test","authors":"Hansong Du, Jiu-fen Liu, X. Luo, Yi Zhang","doi":"10.1109/tdsc.2023.3243907","DOIUrl":"https://doi.org/10.1109/tdsc.2023.3243907","url":null,"abstract":"As the ultimate goal of steganalysis, secret message extraction plays a decisive role in obtaining secret communication evidence and cracking down on criminal activities. For STC (Syndrome-Trellis Codes)-based adaptive steganography, existing pioneering work on secret message extraction: the method based on run test under plaintext embedding may misjudge incorrect stego key as correct stego key, resulting in the failure of extraction. To avoid such a situation, this manuscript proposed a secret message extraction method based on optimal hypothesis test with 100% accuracy under plaintext embedding. First, it is proved that there is a probability distribution difference between the sub-sequence extracted by correct and incorrect stego key. Then, based on the difference, an optimal hypothesis test model is designed to recover the correct stego key. Finally, given the probability of type I and II errors, the sample size and threshold in the hypothesis test are derived. Classic adaptive steganography such as HUGO (Highly Undetectable Steganography) and J-UNIWARD (JPEG Universal Wavelet Relative Distortion) have been conducted experiment, showing that the proposed method can extract message with 100% accuracy and 44 bits sample size, which verifies the correctness of the theorem and the effectiveness of the method.","PeriodicalId":13047,"journal":{"name":"IEEE Transactions on Dependable and Secure Computing","volume":null,"pages":null},"PeriodicalIF":7.3,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62410968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In mobile crowdsensing (MCS), truth discovery (TD) plays an important role in sensing task completion. Most of the existing studies focus on the privacy preservation of mobile users, and the reliability of mobile users is evaluated by their weights which are calculated based on the submitted sensing data. However, if mobile users are unreliable, the submitted sensing data and their weights are also unreliable, which may influence the accuracy of the ground truths of sensing tasks. Therefore, this article proposes a privacy-preserving and reputation-based truth discovery framework named PRTD which can generate the ground truths of sensing tasks with high accuracy while preserving privacy. Specifically, we first preserve sensing data privacy, weight privacy, and reputation value privacy by utilizing the Paillier algorithm and Pedersen commitment. Then, to verify whether the reputation values of mobile users are tampered with and select mobile users that satisfy the corresponding reputation requirements, we design a privacy-preserving reputation verification algorithm based on reputation commitment and zero-knowledge proof and propose a concept of reliability level to select mobile users. Finally, a general TD algorithm with reliability level is presented to improve the accuracy of the ground truths of sensing tasks. Moreover, theoretical analysis and performance evaluation are conducted, and the evaluation results demonstrate that the PRTD framework outperforms the existing TD frameworks in several evaluation metrics in the synthetic dataset and real-world dataset.
{"title":"A Privacy-Preserving and Reputation-Based Truth Discovery Framework in Mobile Crowdsensing","authors":"Yudan Cheng, Jianfeng Ma, Zhiquan Liu, Zhetao Li, Yongdong Wu, Caiqin Dong, Runchuan Li","doi":"10.1109/tdsc.2023.3276976","DOIUrl":"https://doi.org/10.1109/tdsc.2023.3276976","url":null,"abstract":"In mobile crowdsensing (MCS), truth discovery (TD) plays an important role in sensing task completion. Most of the existing studies focus on the privacy preservation of mobile users, and the reliability of mobile users is evaluated by their weights which are calculated based on the submitted sensing data. However, if mobile users are unreliable, the submitted sensing data and their weights are also unreliable, which may influence the accuracy of the ground truths of sensing tasks. Therefore, this article proposes a privacy-preserving and reputation-based truth discovery framework named PRTD which can generate the ground truths of sensing tasks with high accuracy while preserving privacy. Specifically, we first preserve sensing data privacy, weight privacy, and reputation value privacy by utilizing the Paillier algorithm and Pedersen commitment. Then, to verify whether the reputation values of mobile users are tampered with and select mobile users that satisfy the corresponding reputation requirements, we design a privacy-preserving reputation verification algorithm based on reputation commitment and zero-knowledge proof and propose a concept of reliability level to select mobile users. Finally, a general TD algorithm with reliability level is presented to improve the accuracy of the ground truths of sensing tasks. Moreover, theoretical analysis and performance evaluation are conducted, and the evaluation results demonstrate that the PRTD framework outperforms the existing TD frameworks in several evaluation metrics in the synthetic dataset and real-world dataset.","PeriodicalId":13047,"journal":{"name":"IEEE Transactions on Dependable and Secure Computing","volume":null,"pages":null},"PeriodicalIF":7.3,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62413833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Natural forking is tantamount to the “black swan” event in blockchain since it emerges unexpectedly with a small probability, and may incur low resource utilization and costly economic loss. The ongoing literature analyzes natural forking mainly from the macroscopic perspective, which is insufficient to further understand this phenomenon since it roots in the instantaneous difference between block creation and propagation microscopically. Hence, in this article, we fill this gap by leveraging the large deviation theory to conduct the first micro study of natural forking, aiming to reveal its inherent mechanism substantially. Our work is featured by 1) conceptual innovation. We creatively abstract the blockchain overlay network as a “service system”. This allows us to investigate natural forking from the perspective of “supply and demand”. Based on this, we can identify the competitive dynamics of blockchain and construct a queuing model to characterize natural forking; 2) progressiveness. We scrutinize the natural forking probability as well as its decay rate via a three-step scheme from simple to complex, which are the single-source i.i.d. scheme, the single-source non-i.i.d. scheme, and the many-source non-i.i.d. scheme. By doing so, we can answer when and how fast should we take actions and what actions should we take against natural forking. Our valuable findings can not only put forward decisive guidelines theoretically from the top level, but also engineer optimal countermeasures operationally on a practical level to thwart natural forking.
{"title":"Black Swan in Blockchain: Micro Analysis of Natural Forking","authors":"Hongwei Shi, Shengling Wang, Qin Hu, Xiuzhen Cheng","doi":"10.1109/tdsc.2022.3219443","DOIUrl":"https://doi.org/10.1109/tdsc.2022.3219443","url":null,"abstract":"Natural forking is tantamount to the “black swan” event in blockchain since it emerges unexpectedly with a small probability, and may incur low resource utilization and costly economic loss. The ongoing literature analyzes natural forking mainly from the macroscopic perspective, which is insufficient to further understand this phenomenon since it roots in the instantaneous difference between block creation and propagation microscopically. Hence, in this article, we fill this gap by leveraging the large deviation theory to conduct the first micro study of natural forking, aiming to reveal its inherent mechanism substantially. Our work is featured by 1) conceptual innovation. We creatively abstract the blockchain overlay network as a “service system”. This allows us to investigate natural forking from the perspective of “supply and demand”. Based on this, we can identify the competitive dynamics of blockchain and construct a queuing model to characterize natural forking; 2) progressiveness. We scrutinize the natural forking probability as well as its decay rate via a three-step scheme from simple to complex, which are the single-source i.i.d. scheme, the single-source non-i.i.d. scheme, and the many-source non-i.i.d. scheme. By doing so, we can answer when and how fast should we take actions and what actions should we take against natural forking. Our valuable findings can not only put forward decisive guidelines theoretically from the top level, but also engineer optimal countermeasures operationally on a practical level to thwart natural forking.","PeriodicalId":13047,"journal":{"name":"IEEE Transactions on Dependable and Secure Computing","volume":null,"pages":null},"PeriodicalIF":7.3,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62406471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.1109/tdsc.2022.3227650
Kai Zhang, Xiwen Wang, Jianting Ning, M. Wen, Rongxing Lu
Secure cloud search services provide a cost-effective way for resource-constrained clients to search encrypted files in the cloud, where data owners can customize search authorization. Despite providing fine-grained authorization, traditional attribute-based keyword search (ABKS) solutions generally support single keyword search. Towards expressive queries over encrypted data, multi-client searchable symmetric encryption (MC-SSE) was introduced. However, current search authorizations of existing MC-SSEs: (i) cannot support dynamic updating; (ii) are (semi-)black-box implementations of attribute-based encryption; (iii) incur significant cost during system initialization and file encryption. To address these limitations, we present AasBirch, an MC-SSE system with fast fine-grained authorization that supports adaptable authorization switching from one policy to any other one. AasBirch achieves constant-size storage and lightweight time cost for system initialization, file encryption and file searching. We conduct extensive experiments based on Enron dataset in real cloud environment. Compared to state-of-the-art MC-SSE with fine-grained authorization, AasBirch achieves 30$sim 200times$∼200× smaller public parameter and secret key size, with the assumed least frequent keyword in a query ($s$s-term) as 21. Moreover, it runs 10$sim 20times$∼20× faster for file encryption and $>20times$>20× faster for file searching. In addition, AasBirch outperforms 80,000× (resp. 7,850×) faster with $s$s-term=1 (resp. =21), as compared to classic dynamic ABKS system.
{"title":"Multi-Client Boolean File Retrieval with Adaptable Authorization Switching for Secure Cloud Search Services","authors":"Kai Zhang, Xiwen Wang, Jianting Ning, M. Wen, Rongxing Lu","doi":"10.1109/tdsc.2022.3227650","DOIUrl":"https://doi.org/10.1109/tdsc.2022.3227650","url":null,"abstract":"Secure cloud search services provide a cost-effective way for resource-constrained clients to search encrypted files in the cloud, where data owners can customize search authorization. Despite providing fine-grained authorization, traditional attribute-based keyword search (ABKS) solutions generally support single keyword search. Towards expressive queries over encrypted data, multi-client searchable symmetric encryption (MC-SSE) was introduced. However, current search authorizations of existing MC-SSEs: (i) cannot support dynamic updating; (ii) are (semi-)black-box implementations of attribute-based encryption; (iii) incur significant cost during system initialization and file encryption. To address these limitations, we present AasBirch, an MC-SSE system with fast fine-grained authorization that supports adaptable authorization switching from one policy to any other one. AasBirch achieves constant-size storage and lightweight time cost for system initialization, file encryption and file searching. We conduct extensive experiments based on Enron dataset in real cloud environment. Compared to state-of-the-art MC-SSE with fine-grained authorization, AasBirch achieves 30<inline-formula><tex-math notation=\"LaTeX\">$sim 200times$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>∼</mml:mo><mml:mn>200</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"wen-ieq1-3227650.gif\"/></alternatives></inline-formula> smaller public parameter and secret key size, with the assumed least frequent keyword in a query (<inline-formula><tex-math notation=\"LaTeX\">$s$</tex-math><alternatives><mml:math><mml:mi>s</mml:mi></mml:math><inline-graphic xlink:href=\"wen-ieq2-3227650.gif\"/></alternatives></inline-formula>-term) as 21. Moreover, it runs 10<inline-formula><tex-math notation=\"LaTeX\">$sim 20times$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>∼</mml:mo><mml:mn>20</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"wen-ieq3-3227650.gif\"/></alternatives></inline-formula> faster for file encryption and <inline-formula><tex-math notation=\"LaTeX\">$>20times$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>></mml:mo><mml:mn>20</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"wen-ieq4-3227650.gif\"/></alternatives></inline-formula> faster for file searching. In addition, AasBirch outperforms 80,000× (resp. 7,850×) faster with <inline-formula><tex-math notation=\"LaTeX\">$s$</tex-math><alternatives><mml:math><mml:mi>s</mml:mi></mml:math><inline-graphic xlink:href=\"wen-ieq5-3227650.gif\"/></alternatives></inline-formula>-term=1 (resp. =21), as compared to classic dynamic ABKS system.","PeriodicalId":13047,"journal":{"name":"IEEE Transactions on Dependable and Secure Computing","volume":null,"pages":null},"PeriodicalIF":7.3,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62406821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.1109/tdsc.2022.3233915
Yicheng Pan, Meng Ma, Xinrui Jiang, Ping Wang
Today many web applications in the cloud (apps) are built based on microservices. However, as the anomaly propagates in a highly dynamic and complex way, troubleshooting them becomes full of challenges. Existing diagnostic methods are mostly designed based on monitoring metrics retrieved from the microservice system kernel. Therefore, application owners and even site reliability engineers (SREs) cannot effectively resort to those methods when the microservice systems lack such a comprehensive monitoring infrastructure. In this article, we develop DyCause, a crowdsourcing solution to the asymmetric diagnostic information problem. Our solution collects the operational status of kernel services collaboratively from the user space and initiates diagnosis on demand. Without the requirement of any architectural or functional infrastructure, it is both fast and lightweight to deploy DyCause in a microservice system. In order to discover the fine-grained dynamic causalities between services during the anomaly, we also design an efficient algorithm based on statistical analysis. Based on this algorithm, we can also analyze the anomaly propagation paths within the microservice system and generate a better interpretable diagnosis. In our evaluation, we test DyCause in a controlled simulation environment and a real-world cloud system. Our results have shown that DyCause has the best accuracy and efficiency among several state-of-the-art methods and is more robust in terms of parameters.
{"title":"DyCause: Crowdsourcing to Diagnose Microservice Kernel Failure","authors":"Yicheng Pan, Meng Ma, Xinrui Jiang, Ping Wang","doi":"10.1109/tdsc.2022.3233915","DOIUrl":"https://doi.org/10.1109/tdsc.2022.3233915","url":null,"abstract":"Today many web applications in the cloud (apps) are built based on microservices. However, as the anomaly propagates in a highly dynamic and complex way, troubleshooting them becomes full of challenges. Existing diagnostic methods are mostly designed based on monitoring metrics retrieved from the microservice system kernel. Therefore, application owners and even site reliability engineers (SREs) cannot effectively resort to those methods when the microservice systems lack such a comprehensive monitoring infrastructure. In this article, we develop DyCause, a crowdsourcing solution to the asymmetric diagnostic information problem. Our solution collects the operational status of kernel services collaboratively from the user space and initiates diagnosis on demand. Without the requirement of any architectural or functional infrastructure, it is both fast and lightweight to deploy DyCause in a microservice system. In order to discover the fine-grained dynamic causalities between services during the anomaly, we also design an efficient algorithm based on statistical analysis. Based on this algorithm, we can also analyze the anomaly propagation paths within the microservice system and generate a better interpretable diagnosis. In our evaluation, we test DyCause in a controlled simulation environment and a real-world cloud system. Our results have shown that DyCause has the best accuracy and efficiency among several state-of-the-art methods and is more robust in terms of parameters.","PeriodicalId":13047,"journal":{"name":"IEEE Transactions on Dependable and Secure Computing","volume":null,"pages":null},"PeriodicalIF":7.3,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62409303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.1109/tdsc.2023.3234355
Zhuo Ma, Xinjing Liu, Yang Liu, Ximeng Liu, Zhan Qin, Kui Ren
Recently, model stealing attacks are widely studied but most of them are focused on stealing a single non-discrete model, e.g., neural networks. For ensemble models, these attacks are either non-executable or suffer from intolerant performance degradation due to the complex model structure (multiple sub-models) and the discreteness possessed by the sub-model (e.g., decision trees). To overcome the bottleneck, this paper proposes a divide-and-conquer strategy called DivTheft to formulate the model stealing attack to common ensemble models by combining active learning (AL). Specifically, based on the boosting learning concept, we divide a hard ensemble model stealing task into multiple simpler ones about single sub-model stealing. Then, we adopt AL to conquer the data-free sub-model stealing task. During the process, the current AL algorithm easily causes the stolen model to be biased because of ignoring the past useful memories. Thus, DivTheft involves a newly designed uncertainty sampling scheme to filter reusable samples from the previously used ones. Experiments show that compared with the prior work, DivTheft can save almost 50% queries while ensuring a competitive agreement rate to the victim model.
{"title":"DivTheft: An Ensemble Model Stealing Attack by Divide-and-Conquer","authors":"Zhuo Ma, Xinjing Liu, Yang Liu, Ximeng Liu, Zhan Qin, Kui Ren","doi":"10.1109/tdsc.2023.3234355","DOIUrl":"https://doi.org/10.1109/tdsc.2023.3234355","url":null,"abstract":"Recently, model stealing attacks are widely studied but most of them are focused on stealing a single non-discrete model, e.g., neural networks. For ensemble models, these attacks are either non-executable or suffer from intolerant performance degradation due to the complex model structure (multiple sub-models) and the discreteness possessed by the sub-model (e.g., decision trees). To overcome the bottleneck, this paper proposes a divide-and-conquer strategy called DivTheft to formulate the model stealing attack to common ensemble models by combining active learning (AL). Specifically, based on the boosting learning concept, we divide a hard ensemble model stealing task into multiple simpler ones about single sub-model stealing. Then, we adopt AL to conquer the data-free sub-model stealing task. During the process, the current AL algorithm easily causes the stolen model to be biased because of ignoring the past useful memories. Thus, DivTheft involves a newly designed uncertainty sampling scheme to filter reusable samples from the previously used ones. Experiments show that compared with the prior work, DivTheft can save almost 50% queries while ensuring a competitive agreement rate to the victim model.","PeriodicalId":13047,"journal":{"name":"IEEE Transactions on Dependable and Secure Computing","volume":null,"pages":null},"PeriodicalIF":7.3,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62409454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent research has shown that deep neural networks are vulnerable to backdoor attacks. A carefully-designed backdoor trigger will mislead the victim model to misclassify any sample with the trigger to the target label. Nevertheless, existing works usually utilize visible triggers, such as a white square at the corner of the image, which are easily detected by human inspections. Current efforts on developing invisible triggers yield low attack success in the physical domain. In this paper, we propose Kaleidoscope, an RGB (red, green, and blue) filter-based backdoor attack method, which utilizes RGB filter operations as the backdoor trigger. To enhance the attack success rate, we design a novel model-dependent filter trigger generation algorithm. We also introduce two constraints in the loss function to make the backdoored samples more natural and less distorted. Extensive experiments on CIFAR-10, CIFAR-100, ImageNette, and VGG-Flower have demonstrated that RGB filter-processed samples not only achieve high attack success rate but also are unnoticeable to humans. It is shown that Kaleidoscope can reach an attack success rate of more than 84% in the physical world under different lighting intensities and shooting angles. Kaleidoscope is also shown to be robust to state-of-the-art backdoor defenses, such as spectral signature, STRIP, and MNTD.
{"title":"Kaleidoscope: Physical Backdoor Attacks against Deep Neural Networks with RGB Filters","authors":"Xueluan Gong, Ziyao Wang, Yanjiao Chen, Meng Xue, Qianqian Wang, Chao Shen","doi":"10.1109/tdsc.2023.3239225","DOIUrl":"https://doi.org/10.1109/tdsc.2023.3239225","url":null,"abstract":"Recent research has shown that deep neural networks are vulnerable to backdoor attacks. A carefully-designed backdoor trigger will mislead the victim model to misclassify any sample with the trigger to the target label. Nevertheless, existing works usually utilize visible triggers, such as a white square at the corner of the image, which are easily detected by human inspections. Current efforts on developing invisible triggers yield low attack success in the physical domain. In this paper, we propose Kaleidoscope, an RGB (red, green, and blue) filter-based backdoor attack method, which utilizes RGB filter operations as the backdoor trigger. To enhance the attack success rate, we design a novel model-dependent filter trigger generation algorithm. We also introduce two constraints in the loss function to make the backdoored samples more natural and less distorted. Extensive experiments on CIFAR-10, CIFAR-100, ImageNette, and VGG-Flower have demonstrated that RGB filter-processed samples not only achieve high attack success rate but also are unnoticeable to humans. It is shown that Kaleidoscope can reach an attack success rate of more than 84% in the physical world under different lighting intensities and shooting angles. Kaleidoscope is also shown to be robust to state-of-the-art backdoor defenses, such as spectral signature, STRIP, and MNTD.","PeriodicalId":13047,"journal":{"name":"IEEE Transactions on Dependable and Secure Computing","volume":null,"pages":null},"PeriodicalIF":7.3,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62410406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.1109/TDSC.2022.3214235
Hyunsoo Kwon, Sangtae Lee, Minjae Kim, Changhee Hahn, Junbeom Hur
Digital certificates play an important role in the authentication of communicating parties for transport layer security. Recently, however, frequent incidents such as the illegal issuance of fake certificates by a compromised certificate authority have raised concerns about the legacy certificate system. Certificate Transparency (CT) mitigates such issues by employing a log server to audit issued certificates publicly, making the certificate issuance and verification processes transparent. Unfortunately, the legacy CT ecosystem suffers from log server compromises and user browsing information leakage. Furthermore, the data structure for the certificate management in the legacy CT system incurs computation overhead linear to the number of registered certificates in the log. In this paper, we propose a secure CT scheme by leveraging a shared value tree (SVT), a novel log structure specifically designed to address the log server compromise and browsing information leakage problems. The verification time of SVT remains constant regardless of the number of registered certificates in the log. We analyze our scheme on the legacy CT system to demonstrate its incremental deployability, guaranteeing a smooth transition toward a more secure web ecosystem.
{"title":"Certificate Transparency With Enhanced Privacy","authors":"Hyunsoo Kwon, Sangtae Lee, Minjae Kim, Changhee Hahn, Junbeom Hur","doi":"10.1109/TDSC.2022.3214235","DOIUrl":"https://doi.org/10.1109/TDSC.2022.3214235","url":null,"abstract":"Digital certificates play an important role in the authentication of communicating parties for transport layer security. Recently, however, frequent incidents such as the illegal issuance of fake certificates by a compromised certificate authority have raised concerns about the legacy certificate system. Certificate Transparency (CT) mitigates such issues by employing a log server to audit issued certificates publicly, making the certificate issuance and verification processes transparent. Unfortunately, the legacy CT ecosystem suffers from log server compromises and user browsing information leakage. Furthermore, the data structure for the certificate management in the legacy CT system incurs computation overhead linear to the number of registered certificates in the log. In this paper, we propose a secure CT scheme by leveraging a shared value tree (SVT), a novel log structure specifically designed to address the log server compromise and browsing information leakage problems. The verification time of SVT remains constant regardless of the number of registered certificates in the log. We analyze our scheme on the legacy CT system to demonstrate its incremental deployability, guaranteeing a smooth transition toward a more secure web ecosystem.","PeriodicalId":13047,"journal":{"name":"IEEE Transactions on Dependable and Secure Computing","volume":null,"pages":null},"PeriodicalIF":7.3,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49055116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.1109/TDSC.2022.3216297
Safwa Ameer, James O. Benson, R. Sandhu
Smart homes are interconnected homes in which a wide variety of digital devices with limited resources communicate with multiple users and among themselves using multiple protocols. The deployment of resource-limited devices and the use of a wide range of technologies expand the attack surface and position the smart home as a target for many potential security threats. Access control is among the top security challenges in smart home IoT. Several access control models have been developed or adapted for IoT in general, with a few specifically designed for the smart home IoT domain. Most of these models are built on the role-based access control (RBAC) model or the attribute-based access control (ABAC) model. However, recently some researchers demonstrated that the need arises for a hybrid model combining ABAC and RBAC, thereby incorporating the benefits of both models to better meet IoT access control challenges in general and smart homes requirements in particular. In this paper, we used two approaches to develop two different hybrid models for smart home IoT. We followed a role-centric approach and an attribute-centric approach to develop HyBAC$_{RC}$RC and HyBAC$_{AC}$AC, respectively. We formally define these models and illustrate their features through a use case scenario demonstration. We further provide a proof-of-concept implementation for each model in Amazon Web Services (AWS) IoT platform. Finally, we conduct a theoretical comparison between the two models proposed in this paper in addition to the EGRBAC model (RBAC model for smart home IoT) and HABAC model (ABAC model for smart home IoT), which were previously developed to meet smart homes’ challenges.
{"title":"Hybrid Approaches (ABAC and RBAC) Toward Secure Access Control in Smart Home IoT","authors":"Safwa Ameer, James O. Benson, R. Sandhu","doi":"10.1109/TDSC.2022.3216297","DOIUrl":"https://doi.org/10.1109/TDSC.2022.3216297","url":null,"abstract":"Smart homes are interconnected homes in which a wide variety of digital devices with limited resources communicate with multiple users and among themselves using multiple protocols. The deployment of resource-limited devices and the use of a wide range of technologies expand the attack surface and position the smart home as a target for many potential security threats. Access control is among the top security challenges in smart home IoT. Several access control models have been developed or adapted for IoT in general, with a few specifically designed for the smart home IoT domain. Most of these models are built on the role-based access control (RBAC) model or the attribute-based access control (ABAC) model. However, recently some researchers demonstrated that the need arises for a hybrid model combining ABAC and RBAC, thereby incorporating the benefits of both models to better meet IoT access control challenges in general and smart homes requirements in particular. In this paper, we used two approaches to develop two different hybrid models for smart home IoT. We followed a role-centric approach and an attribute-centric approach to develop HyBAC<inline-formula><tex-math notation=\"LaTeX\">$_{RC}$</tex-math><alternatives><mml:math><mml:msub><mml:mrow/><mml:mrow><mml:mi>R</mml:mi><mml:mi>C</mml:mi></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href=\"ameer-ieq1-3216297.gif\"/></alternatives></inline-formula> and HyBAC<inline-formula><tex-math notation=\"LaTeX\">$_{AC}$</tex-math><alternatives><mml:math><mml:msub><mml:mrow/><mml:mrow><mml:mi>A</mml:mi><mml:mi>C</mml:mi></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href=\"ameer-ieq2-3216297.gif\"/></alternatives></inline-formula>, respectively. We formally define these models and illustrate their features through a use case scenario demonstration. We further provide a proof-of-concept implementation for each model in Amazon Web Services (AWS) IoT platform. Finally, we conduct a theoretical comparison between the two models proposed in this paper in addition to the EGRBAC model (RBAC model for smart home IoT) and HABAC model (ABAC model for smart home IoT), which were previously developed to meet smart homes’ challenges.","PeriodicalId":13047,"journal":{"name":"IEEE Transactions on Dependable and Secure Computing","volume":null,"pages":null},"PeriodicalIF":7.3,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48562703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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