The National Science Foundation has made investments in Software Defined Networking (SDN) and Network Function Virtualization (NFV) for many years, in both the research and infrastructure areas. SDN and NFV enable systems to become more open to transformative research, with implications for revolutionary new applications and services. Additionally, the emerging concept of Software-Defined Exchanges will enable large-scale interconnection of Software Defined infrastructures, owned and operated by many different organizations, to provide logically isolated 'on demand' global scale infrastructure on an end-to-end basis, with enhanced flexibility and security for new applications. This talk will examine past NSF investments and successes in SDN/NFV, identify new research opportunities available to the community and present challenges that need to be overcome to make SDN/NFV a reality in operational cyberinfrastructure.
{"title":"SDN Research Challenges and Opportunities","authors":"A. Nikolich","doi":"10.1145/2857705.2857730","DOIUrl":"https://doi.org/10.1145/2857705.2857730","url":null,"abstract":"The National Science Foundation has made investments in Software Defined Networking (SDN) and Network Function Virtualization (NFV) for many years, in both the research and infrastructure areas. SDN and NFV enable systems to become more open to transformative research, with implications for revolutionary new applications and services. Additionally, the emerging concept of Software-Defined Exchanges will enable large-scale interconnection of Software Defined infrastructures, owned and operated by many different organizations, to provide logically isolated 'on demand' global scale infrastructure on an end-to-end basis, with enhanced flexibility and security for new applications. This talk will examine past NSF investments and successes in SDN/NFV, identify new research opportunities available to the community and present challenges that need to be overcome to make SDN/NFV a reality in operational cyberinfrastructure.","PeriodicalId":377412,"journal":{"name":"Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123097462","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}
Teryl Taylor, Xin Hu, Ting Wang, Jiyong Jang, M. Stoecklin, F. Monrose, R. Sailer
Unfortunately, the computers we use for everyday activities can be infiltrated while simply browsing innocuous sites that, unbeknownst to the website owner, may be laden with malicious advertisements. So-called malvertising, redirects browsers to web-based exploit kits that are designed to find vulnerabilities in the browser and subsequently download malicious payloads. We propose a new approach for detecting such malfeasance by leveraging the inherent structural patterns in HTTP traffic to classify exploit kit instances. Our key insight is that an exploit kit leads the browser to download payloads using multiple requests from malicious servers. We capture these interactions in a "tree-like" form, and using a scalable index of malware samples, model the detection process as a subtree similarity search problem. The approach is evaluated on 3800 hours of real-world traffic including over 4 billion flows and reduces false positive rates by four orders of magnitude over current state-of-the-art techniques with comparable true positive rates. We show that our approach can operate in near real-time, and is able to handle peak traffic levels on a large enterprise network --- identifying 28 new exploit kit instances during our analysis period.
{"title":"Detecting Malicious Exploit Kits using Tree-based Similarity Searches","authors":"Teryl Taylor, Xin Hu, Ting Wang, Jiyong Jang, M. Stoecklin, F. Monrose, R. Sailer","doi":"10.1145/2857705.2857718","DOIUrl":"https://doi.org/10.1145/2857705.2857718","url":null,"abstract":"Unfortunately, the computers we use for everyday activities can be infiltrated while simply browsing innocuous sites that, unbeknownst to the website owner, may be laden with malicious advertisements. So-called malvertising, redirects browsers to web-based exploit kits that are designed to find vulnerabilities in the browser and subsequently download malicious payloads. We propose a new approach for detecting such malfeasance by leveraging the inherent structural patterns in HTTP traffic to classify exploit kit instances. Our key insight is that an exploit kit leads the browser to download payloads using multiple requests from malicious servers. We capture these interactions in a \"tree-like\" form, and using a scalable index of malware samples, model the detection process as a subtree similarity search problem. The approach is evaluated on 3800 hours of real-world traffic including over 4 billion flows and reduces false positive rates by four orders of magnitude over current state-of-the-art techniques with comparable true positive rates. We show that our approach can operate in near real-time, and is able to handle peak traffic levels on a large enterprise network --- identifying 28 new exploit kit instances during our analysis period.","PeriodicalId":377412,"journal":{"name":"Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128451899","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}
Organizations need to monitor their business processes to ensure that what actually happens in the system is compliant with the prescribed behavior. Deviations from the prescribed behavior may correspond to violations of security requirements and expose organizations to severe risks. Thus, it is crucial for organizations to detect and address nonconforming behavior as early as possible. In this paper, we present an auditing framework that facilitates the analysis of process executions by detecting nonconforming behaviors and ranking them with respect to their criticality. Our framework employs conformance checking techniques to detect possible explanations of nonconformity. Based on such explanations, the framework assesses the criticality of nonconforming process executions based on historical logging data and context information.
{"title":"Risk-based Analysis of Business Process Executions","authors":"M. Alizadeh, Nicola Zannone","doi":"10.1145/2857705.2857742","DOIUrl":"https://doi.org/10.1145/2857705.2857742","url":null,"abstract":"Organizations need to monitor their business processes to ensure that what actually happens in the system is compliant with the prescribed behavior. Deviations from the prescribed behavior may correspond to violations of security requirements and expose organizations to severe risks. Thus, it is crucial for organizations to detect and address nonconforming behavior as early as possible. In this paper, we present an auditing framework that facilitates the analysis of process executions by detecting nonconforming behaviors and ranking them with respect to their criticality. Our framework employs conformance checking techniques to detect possible explanations of nonconformity. Based on such explanations, the framework assesses the criticality of nonconforming process executions based on historical logging data and context information.","PeriodicalId":377412,"journal":{"name":"Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy","volume":"10 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132090385","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}
Provenance workflows capture movement and transformation of data in complex environments, such as document management in large organizations, content generation and sharing in in social media, scientific computations, etc. Sharing and processing of provenance workflows brings numerous benefits, e.g., improving productivity in an organization, understanding social media interaction patterns, etc. However, directly sharing provenance may also disclose sensitive information such as confidential business practices, or private details about participants in a social network. We propose an algorithm that privately extracts sequential association rules from provenance workflow datasets. Finding such rules has numerous practical applications, such as capacity planning or identifying hot-spots in provenance graphs. Our approach provides good accuracy and strong privacy, by leveraging on the exponential mechanism of differential privacy. We propose an heuristic that identifies promising candidate rules and makes judicious use of the privacy budget. Experimental results show that the our approach is fast and accurate, and clearly outperforms the state-of-the-art. We also identify influential factors in improving accuracy, which helps in choosing promising directions for future improvement.
{"title":"Privacy-Preserving Mining of Sequential Association Rules from Provenance Workflows","authors":"Mihai Maruseac, Gabriel Ghinita","doi":"10.1145/2857705.2857743","DOIUrl":"https://doi.org/10.1145/2857705.2857743","url":null,"abstract":"Provenance workflows capture movement and transformation of data in complex environments, such as document management in large organizations, content generation and sharing in in social media, scientific computations, etc. Sharing and processing of provenance workflows brings numerous benefits, e.g., improving productivity in an organization, understanding social media interaction patterns, etc. However, directly sharing provenance may also disclose sensitive information such as confidential business practices, or private details about participants in a social network. We propose an algorithm that privately extracts sequential association rules from provenance workflow datasets. Finding such rules has numerous practical applications, such as capacity planning or identifying hot-spots in provenance graphs. Our approach provides good accuracy and strong privacy, by leveraging on the exponential mechanism of differential privacy. We propose an heuristic that identifies promising candidate rules and makes judicious use of the privacy budget. Experimental results show that the our approach is fast and accurate, and clearly outperforms the state-of-the-art. We also identify influential factors in improving accuracy, which helps in choosing promising directions for future improvement.","PeriodicalId":377412,"journal":{"name":"Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130928485","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}
Yoshinori Aono, Takuya Hayashi, L. T. Phong, Lihua Wang
Logistic regression is a powerful machine learning tool to classify data. When dealing with sensitive data such as private or medical information, cares are necessary. In this paper, we propose a secure system for protecting the training data in logistic regression via homomorphic encryption. Perhaps surprisingly, despite the non-polynomial tasks of training in logistic regression, we show that only additively homomorphic encryption is needed to build our system. Our system is secure and scalable with the dataset size.
{"title":"Scalable and Secure Logistic Regression via Homomorphic Encryption","authors":"Yoshinori Aono, Takuya Hayashi, L. T. Phong, Lihua Wang","doi":"10.1145/2857705.2857731","DOIUrl":"https://doi.org/10.1145/2857705.2857731","url":null,"abstract":"Logistic regression is a powerful machine learning tool to classify data. When dealing with sensitive data such as private or medical information, cares are necessary. In this paper, we propose a secure system for protecting the training data in logistic regression via homomorphic encryption. Perhaps surprisingly, despite the non-polynomial tasks of training in logistic regression, we show that only additively homomorphic encryption is needed to build our system. Our system is secure and scalable with the dataset size.","PeriodicalId":377412,"journal":{"name":"Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124914646","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}
John Maheswaran, Daniel Jackowitz, Ennan Zhai, D. Wolinsky, B. Ford
Federated identity providers, e.g., Facebook and PayPal, offer a convenient means for authenticating users to third-party applications. Unfortunately such cross-site authentications carry privacy and tracking risks. For example, federated identity providers can learn what applications users are accessing; meanwhile, the applications can know the users' identities in reality. This paper presents Crypto-Book, an anonymizing layer enabling federated identity authentications while preventing these risks. Crypto-Book uses a set of independently managed servers that employ a (t,n)-threshold cryptosystem to collectively assign credentials to each federated identity (in the form of either a public/private keypair or blinded signed messages). With the credentials in hand, clients can then leverage anonymous authentication techniques such as linkable ring signatures or partially blind signatures to log into third-party applications in an anonymous yet accountable way. We have implemented a prototype of Crypto-Book and demonstrated its use with three applications: a Wiki system, an anonymous group communication system, and a whistleblower submission system. Crypto-Book is practical and has low overhead: in a deployment within our research group, Crypto-Book group authentication took 1.607s end-to-end, an overhead of 1.2s compared to traditional non-privacy-preserving federated authentication.
{"title":"Building Privacy-Preserving Cryptographic Credentials from Federated Online Identities","authors":"John Maheswaran, Daniel Jackowitz, Ennan Zhai, D. Wolinsky, B. Ford","doi":"10.1145/2857705.2857725","DOIUrl":"https://doi.org/10.1145/2857705.2857725","url":null,"abstract":"Federated identity providers, e.g., Facebook and PayPal, offer a convenient means for authenticating users to third-party applications. Unfortunately such cross-site authentications carry privacy and tracking risks. For example, federated identity providers can learn what applications users are accessing; meanwhile, the applications can know the users' identities in reality. This paper presents Crypto-Book, an anonymizing layer enabling federated identity authentications while preventing these risks. Crypto-Book uses a set of independently managed servers that employ a (t,n)-threshold cryptosystem to collectively assign credentials to each federated identity (in the form of either a public/private keypair or blinded signed messages). With the credentials in hand, clients can then leverage anonymous authentication techniques such as linkable ring signatures or partially blind signatures to log into third-party applications in an anonymous yet accountable way. We have implemented a prototype of Crypto-Book and demonstrated its use with three applications: a Wiki system, an anonymous group communication system, and a whistleblower submission system. Crypto-Book is practical and has low overhead: in a deployment within our research group, Crypto-Book group authentication took 1.607s end-to-end, an overhead of 1.2s compared to traditional non-privacy-preserving federated authentication.","PeriodicalId":377412,"journal":{"name":"Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy","volume":"433 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123007560","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}
Ibéria Medeiros, Miguel Beatriz, N. Neves, M. Correia
After more than a decade of research, web application security continues to be a challenge and the backend database the most appetizing target. The paper proposes preventing injection attacks against the database management system (DBMS) behind web applications by embedding protections in the DBMS itself. The motivation is twofold. First, the approach of embedding protections in operating systems and applications running on top of them has been effective to protect this software. Second, there is a semantic mismatch between how SQL queries are believed to be executed by the DBMS and how they are actually executed, leading to subtle vulnerabilities in prevention mechanisms. The approach -- SEPTIC -- was implemented in MySQL and evaluated experimentally with web applications written in PHP and Java/Spring. In the evaluation SEPTIC has shown neither false negatives nor false positives, on the contrary of alternative approaches, causing also a low performance overhead in the order of 2.2%.
{"title":"Hacking the DBMS to Prevent Injection Attacks","authors":"Ibéria Medeiros, Miguel Beatriz, N. Neves, M. Correia","doi":"10.1145/2857705.2857723","DOIUrl":"https://doi.org/10.1145/2857705.2857723","url":null,"abstract":"After more than a decade of research, web application security continues to be a challenge and the backend database the most appetizing target. The paper proposes preventing injection attacks against the database management system (DBMS) behind web applications by embedding protections in the DBMS itself. The motivation is twofold. First, the approach of embedding protections in operating systems and applications running on top of them has been effective to protect this software. Second, there is a semantic mismatch between how SQL queries are believed to be executed by the DBMS and how they are actually executed, leading to subtle vulnerabilities in prevention mechanisms. The approach -- SEPTIC -- was implemented in MySQL and evaluated experimentally with web applications written in PHP and Java/Spring. In the evaluation SEPTIC has shown neither false negatives nor false positives, on the contrary of alternative approaches, causing also a low performance overhead in the order of 2.2%.","PeriodicalId":377412,"journal":{"name":"Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134300364","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}
Taous Madi, Suryadipta Majumdar, Yushun Wang, Yosr Jarraya, M. Pourzandi, Lingyu Wang
Cloud service providers typically adopt the multi-tenancy model to optimize resources usage and achieve the promised cost-effectiveness. Sharing resources between different tenants and the underlying complex technology increase the necessity of transparency and accountability. In this regard, auditing security compliance of the provider's infrastructure against standards, regulations and customers' policies takes on an increasing importance in the cloud to boost the trust between the stakeholders. However, virtualization and scalability make compliance verification challenging. In this work, we propose an automated framework that allows auditing the cloud infrastructure from the structural point of view while focusing on virtualization-related security properties and consistency between multiple control layers. Furthermore, to show the feasibility of our approach, we integrate our auditing system into OpenStack, one of the most used cloud infrastructure management systems. To show the scalability and validity of our framework, we present our experimental results on assessing several properties related to auditing inter-layer consistency, virtual machines co-residence, and virtual resources isolation.
{"title":"Auditing Security Compliance of the Virtualized Infrastructure in the Cloud: Application to OpenStack","authors":"Taous Madi, Suryadipta Majumdar, Yushun Wang, Yosr Jarraya, M. Pourzandi, Lingyu Wang","doi":"10.1145/2857705.2857721","DOIUrl":"https://doi.org/10.1145/2857705.2857721","url":null,"abstract":"Cloud service providers typically adopt the multi-tenancy model to optimize resources usage and achieve the promised cost-effectiveness. Sharing resources between different tenants and the underlying complex technology increase the necessity of transparency and accountability. In this regard, auditing security compliance of the provider's infrastructure against standards, regulations and customers' policies takes on an increasing importance in the cloud to boost the trust between the stakeholders. However, virtualization and scalability make compliance verification challenging. In this work, we propose an automated framework that allows auditing the cloud infrastructure from the structural point of view while focusing on virtualization-related security properties and consistency between multiple control layers. Furthermore, to show the feasibility of our approach, we integrate our auditing system into OpenStack, one of the most used cloud infrastructure management systems. To show the scalability and validity of our framework, we present our experimental results on assessing several properties related to auditing inter-layer consistency, virtual machines co-residence, and virtual resources isolation.","PeriodicalId":377412,"journal":{"name":"Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114980081","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}
Modern OS kernels including Windows, Linux, and Mac OS all have adopted kernel Address Space Layout Randomization (ASLR), which shifts the base address of kernel code and data into different locations in different runs. Consequently, when performing introspection or forensic analysis of kernel memory, we cannot use any pre-determined addresses to interpret the kernel events. Instead, we must derandomize the address space layout and use the new addresses. However, few efforts have been made to derandomize the kernel address space and yet there are many questions left such as which approach is more efficient and robust. Therefore, we present the first systematic study of how to derandomize a kernel when given a memory snapshot of a running kernel instance. Unlike the derandomization approaches used in traditional memory exploits in which only remote access is available, with introspection and forensics applications, we can use all the information available in kernel memory to generate signatures and derandomize the ASLR. In other words, there exists a large volume of solutions for this problem. As such, in this paper we examine a number of typical approaches to generate strong signatures from both kernel code and data based on the insight of how kernel code and data is updated, and compare them from efficiency (in terms of simplicity, speed etc.) and robustness (e.g., whether the approach is hard to be evaded or forged) perspective. In particular, we have designed four approaches including brute-force code scanning, patched code signature generation, unpatched code signature generation, and read-only pointer based approach, according to the intrinsic behavior of kernel code and data with respect to kernel ASLR. We have gained encouraging results for each of these approaches and the corresponding experimental results are reported in this paper.
{"title":"Derandomizing Kernel Address Space Layout for Memory Introspection and Forensics","authors":"Yufei Gu, Zhiqiang Lin","doi":"10.1145/2857705.2857707","DOIUrl":"https://doi.org/10.1145/2857705.2857707","url":null,"abstract":"Modern OS kernels including Windows, Linux, and Mac OS all have adopted kernel Address Space Layout Randomization (ASLR), which shifts the base address of kernel code and data into different locations in different runs. Consequently, when performing introspection or forensic analysis of kernel memory, we cannot use any pre-determined addresses to interpret the kernel events. Instead, we must derandomize the address space layout and use the new addresses. However, few efforts have been made to derandomize the kernel address space and yet there are many questions left such as which approach is more efficient and robust. Therefore, we present the first systematic study of how to derandomize a kernel when given a memory snapshot of a running kernel instance. Unlike the derandomization approaches used in traditional memory exploits in which only remote access is available, with introspection and forensics applications, we can use all the information available in kernel memory to generate signatures and derandomize the ASLR. In other words, there exists a large volume of solutions for this problem. As such, in this paper we examine a number of typical approaches to generate strong signatures from both kernel code and data based on the insight of how kernel code and data is updated, and compare them from efficiency (in terms of simplicity, speed etc.) and robustness (e.g., whether the approach is hard to be evaded or forged) perspective. In particular, we have designed four approaches including brute-force code scanning, patched code signature generation, unpatched code signature generation, and read-only pointer based approach, according to the intrinsic behavior of kernel code and data with respect to kernel ASLR. We have gained encouraging results for each of these approaches and the corresponding experimental results are reported in this paper.","PeriodicalId":377412,"journal":{"name":"Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122948653","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}
The RPPM model of access control uses relationships, paths and principal-matching in order to make access control decisions for general computing systems. Recently Stoller introduced a variant of an early RPPM model supporting administrative actions. Stoller's RPPM^2 model is able to make authorization decisions in respect of actions which affect the system graph and some policy elements. We also see utility in the RPPM model and believe that providing effective administration of the access control model is key to increasing the model's usefulness to real-world implementations. However, whilst we find inspiration in some aspects of Stoller's work, we believe that an alternative approach making use of the latest RPPM model as its basis will offer a wider range of operational and administrative capabilities. We motivate this work with specific requirements for an administrative model and then propose a decentralised discretionary access control approach to administration, whereby users are able to manage model components in the system graph through the addition and deletion of edges. The resulting Administrative RPPM (ARPPM) model supports administration of all of the model's components: the system model, the system graph, the authorization policies and all of their elements.
{"title":"ARPPM: Administration in the RPPM Model","authors":"J. Crampton, J. Sellwood","doi":"10.1145/2857705.2857711","DOIUrl":"https://doi.org/10.1145/2857705.2857711","url":null,"abstract":"The RPPM model of access control uses relationships, paths and principal-matching in order to make access control decisions for general computing systems. Recently Stoller introduced a variant of an early RPPM model supporting administrative actions. Stoller's RPPM^2 model is able to make authorization decisions in respect of actions which affect the system graph and some policy elements. We also see utility in the RPPM model and believe that providing effective administration of the access control model is key to increasing the model's usefulness to real-world implementations. However, whilst we find inspiration in some aspects of Stoller's work, we believe that an alternative approach making use of the latest RPPM model as its basis will offer a wider range of operational and administrative capabilities. We motivate this work with specific requirements for an administrative model and then propose a decentralised discretionary access control approach to administration, whereby users are able to manage model components in the system graph through the addition and deletion of edges. The resulting Administrative RPPM (ARPPM) model supports administration of all of the model's components: the system model, the system graph, the authorization policies and all of their elements.","PeriodicalId":377412,"journal":{"name":"Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124025695","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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