Pub Date : 2019-12-01DOI: 10.1109/TPS-ISA48467.2019.00020
Josh Payne, A. Kundu
In cloud computing environments with many virtual machines, containers, and other systems, an epidemic of malware can be crippling and highly threatening to business processes. In this vision paper, we introduce a hierarchical approach to performing malware detection and analysis using several recent advances in machine learning on graphs, hypergraphs, and natural language. We analyze individual systems and their logs, inspecting and understanding their behavior with attentional sequence models. Given a feature representation of each system's logs using this procedure, we construct an attributed network of the cloud with systems and other components as vertices and propose an analysis of malware with inductive graph and hypergraph learning models. With this foundation, we consider the multicloud case, in which multiple clouds with differing privacy requirements cooperate against the spread of malware, proposing the use of federated learning to perform inference and training while preserving privacy. Finally, we discuss several open problems that remain in defending cloud computing environments against malware related to designing robust ecosystems, identifying cloud-specific optimization problems for response strategy, action spaces for malware containment and eradication, and developing priors and transfer learning tasks for machine learning models in this area.
{"title":"Towards Deep Federated Defenses Against Malware in Cloud Ecosystems","authors":"Josh Payne, A. Kundu","doi":"10.1109/TPS-ISA48467.2019.00020","DOIUrl":"https://doi.org/10.1109/TPS-ISA48467.2019.00020","url":null,"abstract":"In cloud computing environments with many virtual machines, containers, and other systems, an epidemic of malware can be crippling and highly threatening to business processes. In this vision paper, we introduce a hierarchical approach to performing malware detection and analysis using several recent advances in machine learning on graphs, hypergraphs, and natural language. We analyze individual systems and their logs, inspecting and understanding their behavior with attentional sequence models. Given a feature representation of each system's logs using this procedure, we construct an attributed network of the cloud with systems and other components as vertices and propose an analysis of malware with inductive graph and hypergraph learning models. With this foundation, we consider the multicloud case, in which multiple clouds with differing privacy requirements cooperate against the spread of malware, proposing the use of federated learning to perform inference and training while preserving privacy. Finally, we discuss several open problems that remain in defending cloud computing environments against malware related to designing robust ecosystems, identifying cloud-specific optimization problems for response strategy, action spaces for malware containment and eradication, and developing priors and transfer learning tasks for machine learning models in this area.","PeriodicalId":129820,"journal":{"name":"2019 First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123577434","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 : 2019-12-01DOI: 10.1109/TPS-ISA48467.2019.00026
Naresh Adhikari, Naila Bushra, M. Ramkumar
Trust in the integrity of processes for congressional redistricting is crucial for the smooth functioning of democracies. Achieving universal consensus on the fairness and unbiasedness of plans is essential. We propose a novel framework of redistricting to enhance public participation in a redistricting process and bolster transparency in the process outcome. This framework is designed to support submitting a redistricting problem in a blockchain network, submit any number of districting plan for a redistricting problem, and evaluating the plans in the network. Moreover, the framework facilitate to choose the "best" of any number of independent redistricting plans, based on agreed-upon metrics like isoperimetric ratio, area moment, population moment, of the proposed districts, among others.
{"title":"Redistricting using Blockchain Network","authors":"Naresh Adhikari, Naila Bushra, M. Ramkumar","doi":"10.1109/TPS-ISA48467.2019.00026","DOIUrl":"https://doi.org/10.1109/TPS-ISA48467.2019.00026","url":null,"abstract":"Trust in the integrity of processes for congressional redistricting is crucial for the smooth functioning of democracies. Achieving universal consensus on the fairness and unbiasedness of plans is essential. We propose a novel framework of redistricting to enhance public participation in a redistricting process and bolster transparency in the process outcome. This framework is designed to support submitting a redistricting problem in a blockchain network, submit any number of districting plan for a redistricting problem, and evaluating the plans in the network. Moreover, the framework facilitate to choose the \"best\" of any number of independent redistricting plans, based on agreed-upon metrics like isoperimetric ratio, area moment, population moment, of the proposed districts, among others.","PeriodicalId":129820,"journal":{"name":"2019 First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131311601","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 : 2019-12-01DOI: 10.1109/TPS-ISA48467.2019.00035
Murat Kantarcioglu, Fahad Shaon
Increasingly organizations are collecting ever larger amounts of data to build complex data analytics, machine learning and AI models. Furthermore, the data needed for building such models may be unstructured (e.g., text, image, and video). Hence such data may be stored in different data management systems ranging from relational databases to newer NoSQL databases tailored for storing unstructured data. Furthermore, data scientists are increasingly using programming languages such as Python, R etc. to process data using many existing libraries. In some cases, the developed code will be automatically executed by the NoSQL system on the stored data. These developments indicate the need for a data security and privacy solution that can uniformly protect data stored in many different data management systems and enforce security policies even if sensitive data is processed using a data scientist submitted complex program. In this paper, we introduce our vision for building such a solution for protecting big data. Specifically, our proposed system system allows organizations to 1) enforce policies that control access to sensitive data, 2) keep necessary audit logs automatically for data governance and regulatory compliance, 3) sanitize and redact sensitive data on-the-fly based on the data sensitivity and AI model needs, 4) detect potentially unauthorized or anomalous access to sensitive data, 5) automatically create attribute-based access control policies based on data sensitivity and data type.
{"title":"Securing Big Data in the Age of AI","authors":"Murat Kantarcioglu, Fahad Shaon","doi":"10.1109/TPS-ISA48467.2019.00035","DOIUrl":"https://doi.org/10.1109/TPS-ISA48467.2019.00035","url":null,"abstract":"Increasingly organizations are collecting ever larger amounts of data to build complex data analytics, machine learning and AI models. Furthermore, the data needed for building such models may be unstructured (e.g., text, image, and video). Hence such data may be stored in different data management systems ranging from relational databases to newer NoSQL databases tailored for storing unstructured data. Furthermore, data scientists are increasingly using programming languages such as Python, R etc. to process data using many existing libraries. In some cases, the developed code will be automatically executed by the NoSQL system on the stored data. These developments indicate the need for a data security and privacy solution that can uniformly protect data stored in many different data management systems and enforce security policies even if sensitive data is processed using a data scientist submitted complex program. In this paper, we introduce our vision for building such a solution for protecting big data. Specifically, our proposed system system allows organizations to 1) enforce policies that control access to sensitive data, 2) keep necessary audit logs automatically for data governance and regulatory compliance, 3) sanitize and redact sensitive data on-the-fly based on the data sensitivity and AI model needs, 4) detect potentially unauthorized or anomalous access to sensitive data, 5) automatically create attribute-based access control policies based on data sensitivity and data type.","PeriodicalId":129820,"journal":{"name":"2019 First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128881846","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 : 2019-12-01DOI: 10.1109/TPS-ISA48467.2019.00021
Feng Wei, U. T. Nguyen
Twitter is a web application playing dual roles of online social networking and micro-blogging. The popularity and open structure of Twitter have attracted a large number of automated programs, known as bots. Legitimate bots generate a large amount of benign contextual content, i.e., tweets delivering news and updating feeds, while malicious bots spread spam or malicious contents. To assist human users in identifying who they are interacting with, this paper focuses on the classification of human and spambot accounts on Twitter, by employing recurrent neural networks, specifically bidirectional Long Short-term Memory (BiLSTM), to efficiently capture features across tweets. To the best of our knowledge, our work is the first that develops a recurrent neural model with word embeddings to distinguish Twitter bots from human accounts, that requires no prior knowledge or assumption about users' profiles, friendship networks, or historical behavior on the target account. Moreover, our model does not require any handcrafted features. The preliminary simulation results are very encouraging. Experiments on the cresci-2017 dataset show that our approach can achieve competitive performance compared with existing state-of-the-art bot detection systems.
{"title":"Twitter Bot Detection Using Bidirectional Long Short-Term Memory Neural Networks and Word Embeddings","authors":"Feng Wei, U. T. Nguyen","doi":"10.1109/TPS-ISA48467.2019.00021","DOIUrl":"https://doi.org/10.1109/TPS-ISA48467.2019.00021","url":null,"abstract":"Twitter is a web application playing dual roles of online social networking and micro-blogging. The popularity and open structure of Twitter have attracted a large number of automated programs, known as bots. Legitimate bots generate a large amount of benign contextual content, i.e., tweets delivering news and updating feeds, while malicious bots spread spam or malicious contents. To assist human users in identifying who they are interacting with, this paper focuses on the classification of human and spambot accounts on Twitter, by employing recurrent neural networks, specifically bidirectional Long Short-term Memory (BiLSTM), to efficiently capture features across tweets. To the best of our knowledge, our work is the first that develops a recurrent neural model with word embeddings to distinguish Twitter bots from human accounts, that requires no prior knowledge or assumption about users' profiles, friendship networks, or historical behavior on the target account. Moreover, our model does not require any handcrafted features. The preliminary simulation results are very encouraging. Experiments on the cresci-2017 dataset show that our approach can achieve competitive performance compared with existing state-of-the-art bot detection systems.","PeriodicalId":129820,"journal":{"name":"2019 First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128161479","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 : 2019-12-01DOI: 10.1109/TPS-ISA48467.2019.00028
Ashwini Rao, Juergen Pfeffer
We investigate the role of Internet tracking in siphoning users' personal data from one country to another. We use the term "siphon" to indicate a one-way channel that once set up will result in a continuous flow of personal information from the source to the destination. We conduct a web privacy measurement study using a Germany-Russia scenario; we collect and analyze tracker data from 12 mainstream news sites in Germany, 1000 top sites in Germany and Russia, and 1000000 top sites in the world. We identify five tracking patterns that can siphon data from users in Germany to Russia; two key parameters of the tracking patterns, distance-to-data and type-of-control, determine timeliness, accuracy and granularity of siphoned data. Results show that Russian trackers are widespread on German news sites. Lastly, we discuss the impact of data siphoning on General Data Protection Regulation (GDPR). Analyses show that tracking patterns can facilitate siphoning of personal data across borders while subverting requirements of GDPR.
{"title":"Data Siphoning Across Borders: The Role of Internet Tracking","authors":"Ashwini Rao, Juergen Pfeffer","doi":"10.1109/TPS-ISA48467.2019.00028","DOIUrl":"https://doi.org/10.1109/TPS-ISA48467.2019.00028","url":null,"abstract":"We investigate the role of Internet tracking in siphoning users' personal data from one country to another. We use the term \"siphon\" to indicate a one-way channel that once set up will result in a continuous flow of personal information from the source to the destination. We conduct a web privacy measurement study using a Germany-Russia scenario; we collect and analyze tracker data from 12 mainstream news sites in Germany, 1000 top sites in Germany and Russia, and 1000000 top sites in the world. We identify five tracking patterns that can siphon data from users in Germany to Russia; two key parameters of the tracking patterns, distance-to-data and type-of-control, determine timeliness, accuracy and granularity of siphoned data. Results show that Russian trackers are widespread on German news sites. Lastly, we discuss the impact of data siphoning on General Data Protection Regulation (GDPR). Analyses show that tracking patterns can facilitate siphoning of personal data across borders while subverting requirements of GDPR.","PeriodicalId":129820,"journal":{"name":"2019 First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117170922","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 : 2019-12-01DOI: 10.1109/TPS-ISA48467.2019.00037
Md Shihabul Islam, H. Verma, L. Khan, Murat Kantarcioglu
The growing adoption of IoT devices in our daily life engendered a need for secure systems to safely store and analyze sensitive data as well as the real-time data processing system to be as fast as possible. The cloud services used to store and process sensitive data are often come out to be vulnerable to outside threats. Furthermore, to analyze streaming IoT data swiftly, they are in need of a fast and efficient system. The Paper will envision the aspects of complexity dealing with real time data from various devices in parallel, building solution to ingest data from different IOT devices, forming a secure platform to process data in a short time, and using various techniques of IOT edge computing to provide meaningful intuitive results to users. The paper envisions two modules of building a real time data analytics system. In the first module, we propose to maintain confidentiality and integrity of IoT data, which is of paramount importance, and manage large-scale data analytics with real-time data collection from various IoT devices in parallel. We envision a framework to preserve data privacy utilizing Trusted Execution Environment (TEE) such as Intel SGX, end-to-end data encryption mechanism, and strong access control policies. Moreover, we design a generic framework to simplify the process of collecting and storing heterogeneous data coming from diverse IoT devices. In the second module, we envision a drone-based data processing system in real-time using edge computing and on-device computing. As, we know the use of drones is growing rapidly across many application domains including real-time monitoring, remote sensing, search and rescue, delivery of goods, security and surveillance, civil infrastructure inspection etc. This paper demonstrates the potential drone applications and their challenges discussing current research trends and provide future insights for potential use cases using edge and on-device computing.
{"title":"Secure Real-Time Heterogeneous IoT Data Management System","authors":"Md Shihabul Islam, H. Verma, L. Khan, Murat Kantarcioglu","doi":"10.1109/TPS-ISA48467.2019.00037","DOIUrl":"https://doi.org/10.1109/TPS-ISA48467.2019.00037","url":null,"abstract":"The growing adoption of IoT devices in our daily life engendered a need for secure systems to safely store and analyze sensitive data as well as the real-time data processing system to be as fast as possible. The cloud services used to store and process sensitive data are often come out to be vulnerable to outside threats. Furthermore, to analyze streaming IoT data swiftly, they are in need of a fast and efficient system. The Paper will envision the aspects of complexity dealing with real time data from various devices in parallel, building solution to ingest data from different IOT devices, forming a secure platform to process data in a short time, and using various techniques of IOT edge computing to provide meaningful intuitive results to users. The paper envisions two modules of building a real time data analytics system. In the first module, we propose to maintain confidentiality and integrity of IoT data, which is of paramount importance, and manage large-scale data analytics with real-time data collection from various IoT devices in parallel. We envision a framework to preserve data privacy utilizing Trusted Execution Environment (TEE) such as Intel SGX, end-to-end data encryption mechanism, and strong access control policies. Moreover, we design a generic framework to simplify the process of collecting and storing heterogeneous data coming from diverse IoT devices. In the second module, we envision a drone-based data processing system in real-time using edge computing and on-device computing. As, we know the use of drones is growing rapidly across many application domains including real-time monitoring, remote sensing, search and rescue, delivery of goods, security and surveillance, civil infrastructure inspection etc. This paper demonstrates the potential drone applications and their challenges discussing current research trends and provide future insights for potential use cases using edge and on-device computing.","PeriodicalId":129820,"journal":{"name":"2019 First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127715496","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 : 2019-12-01DOI: 10.1109/TPS-ISA48467.2019.00018
Yasamin Alagrash, Nithasha Mohan, Sandhya Rani Gollapalli, J. Rrushi
Malware often act on a compromised machine with the identifier of a legitimate user. We analyzed numerous malware and user tasks, and found subtle differences between how the two operate on a machine. We have developed a machine learning approach that characterizes user tasks through their resource utilization. We have found that many routine user tasks retain their resource utilization patterns, despite the occurrence of new dynamics each time a user carries out those tasks. On the other hand, upon landing on a target machine, malware perform a substantial amount of work to explore the machine and discover resources that are of interest to threat actors. Our approach collects live performance counter data from the operating system kernel, and subsequently pre-processes and analyzes those data to learn and then recognize the resource utilization of a task. We develop decoy process mechanisms that camouflage performance counter data to prevent malware from learning the resource utilization of a user task. We tested our approach against both legitimate users in real-world work settings and malware samples, and discuss our findings in the paper.
{"title":"Machine Learning and Recognition of User Tasks for Malware Detection","authors":"Yasamin Alagrash, Nithasha Mohan, Sandhya Rani Gollapalli, J. Rrushi","doi":"10.1109/TPS-ISA48467.2019.00018","DOIUrl":"https://doi.org/10.1109/TPS-ISA48467.2019.00018","url":null,"abstract":"Malware often act on a compromised machine with the identifier of a legitimate user. We analyzed numerous malware and user tasks, and found subtle differences between how the two operate on a machine. We have developed a machine learning approach that characterizes user tasks through their resource utilization. We have found that many routine user tasks retain their resource utilization patterns, despite the occurrence of new dynamics each time a user carries out those tasks. On the other hand, upon landing on a target machine, malware perform a substantial amount of work to explore the machine and discover resources that are of interest to threat actors. Our approach collects live performance counter data from the operating system kernel, and subsequently pre-processes and analyzes those data to learn and then recognize the resource utilization of a task. We develop decoy process mechanisms that camouflage performance counter data to prevent malware from learning the resource utilization of a user task. We tested our approach against both legitimate users in real-world work settings and malware samples, and discuss our findings in the paper.","PeriodicalId":129820,"journal":{"name":"2019 First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124531285","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 : 2019-12-01DOI: 10.1109/TPS-ISA48467.2019.00036
Abhishek Malvankar, Josh Payne, K. K. Budhraja, A. Kundu, Suresh Chari, M. Mohania
Malware is pervasive and poses serious threats to normal operation of business processes in cloud. Cloud computing environments typically have hundreds of hosts that are connected to each other, often with high risk trust assumptions and/or protection mechanisms that are not difficult to break. Malware often exploits such weaknesses, as its immediate goal is often to spread itself to as many hosts as possible. Detecting this propagation is often difficult to address because the malware may reside in multiple components across the software or hardware stack. In this scenario, it is usually best to contain the malware to the smallest possible number of hosts, and it's also critical for system administration to resolve the issue in a timely manner. Furthermore, resolution often requires that several participants across different organizational teams scramble together to address the intrusion. In this vision paper, we define this problem in detail. We then present our vision of decentralized malware containment and the challenges and issues associated with this vision. The approach of containment involves detection and response using graph analytics coupled with a blockchain framework. We propose the use of a dominance frontier for profile nodes which must be involved in the containment process. Smart contracts are used to obtain consensus amongst the involved parties. The paper presents a basic implementation of this proposal. We have further discussed some open problems related to our vision.
{"title":"Malware Containment in Cloud","authors":"Abhishek Malvankar, Josh Payne, K. K. Budhraja, A. Kundu, Suresh Chari, M. Mohania","doi":"10.1109/TPS-ISA48467.2019.00036","DOIUrl":"https://doi.org/10.1109/TPS-ISA48467.2019.00036","url":null,"abstract":"Malware is pervasive and poses serious threats to normal operation of business processes in cloud. Cloud computing environments typically have hundreds of hosts that are connected to each other, often with high risk trust assumptions and/or protection mechanisms that are not difficult to break. Malware often exploits such weaknesses, as its immediate goal is often to spread itself to as many hosts as possible. Detecting this propagation is often difficult to address because the malware may reside in multiple components across the software or hardware stack. In this scenario, it is usually best to contain the malware to the smallest possible number of hosts, and it's also critical for system administration to resolve the issue in a timely manner. Furthermore, resolution often requires that several participants across different organizational teams scramble together to address the intrusion. In this vision paper, we define this problem in detail. We then present our vision of decentralized malware containment and the challenges and issues associated with this vision. The approach of containment involves detection and response using graph analytics coupled with a blockchain framework. We propose the use of a dominance frontier for profile nodes which must be involved in the containment process. Smart contracts are used to obtain consensus amongst the involved parties. The paper presents a basic implementation of this proposal. We have further discussed some open problems related to our vision.","PeriodicalId":129820,"journal":{"name":"2019 First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130912315","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 : 2019-12-01DOI: 10.1109/TPS-ISA48467.2019.00010
Mehrnoosh Shakarami, R. Sandhu
Attribute-based Access Control (ABAC) systems make access decisions utilizing attributes of subjects, objects and environment with respect to a policy. Acquiring real-time values of these attributes is not practical in distributed multi-authority environments due to cost and performance considerations as well as intrinsic delays of distributed systems. So it is possible to make decisions based on outdated policy and attribute values resulting in access violations. This is known as the safety and consistency problem. This problem has been previously studied in trust negotiation and ABAC context. Previous works have assumed attributes to be immutable, to wit their values could be changed only via administrative actions. However, so far there is no research carried out in the context of mutable attributes, values of which could be changed as a result of users access. In this paper we investigate safety and consistency in the context of mutable subject attributes which introduces additional complexity to the problem. In particular, there might be multiple concurrent sessions manipulating the same mutable attribute. Therefore, in addition to exposure of the decision point to stale attribute values, safety and consistency can be compromised due to concurrent utilization of the same attribute. While the general consistency problem has vast literature in distributed systems arena, practical solutions are typically dependent on the specific application domain. We identify two categories of use cases of practical benefit in context of ABAC, which turn out to be amenable to quota-based solutions. We provide a formal analysis of the resulting solutions.
{"title":"Safety and Consistency of Mutable Attributes Using Quotas: A Formal Analysis","authors":"Mehrnoosh Shakarami, R. Sandhu","doi":"10.1109/TPS-ISA48467.2019.00010","DOIUrl":"https://doi.org/10.1109/TPS-ISA48467.2019.00010","url":null,"abstract":"Attribute-based Access Control (ABAC) systems make access decisions utilizing attributes of subjects, objects and environment with respect to a policy. Acquiring real-time values of these attributes is not practical in distributed multi-authority environments due to cost and performance considerations as well as intrinsic delays of distributed systems. So it is possible to make decisions based on outdated policy and attribute values resulting in access violations. This is known as the safety and consistency problem. This problem has been previously studied in trust negotiation and ABAC context. Previous works have assumed attributes to be immutable, to wit their values could be changed only via administrative actions. However, so far there is no research carried out in the context of mutable attributes, values of which could be changed as a result of users access. In this paper we investigate safety and consistency in the context of mutable subject attributes which introduces additional complexity to the problem. In particular, there might be multiple concurrent sessions manipulating the same mutable attribute. Therefore, in addition to exposure of the decision point to stale attribute values, safety and consistency can be compromised due to concurrent utilization of the same attribute. While the general consistency problem has vast literature in distributed systems arena, practical solutions are typically dependent on the specific application domain. We identify two categories of use cases of practical benefit in context of ABAC, which turn out to be amenable to quota-based solutions. We provide a formal analysis of the resulting solutions.","PeriodicalId":129820,"journal":{"name":"2019 First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125424539","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 : 2019-12-01DOI: 10.1109/TPS-ISA48467.2019.00024
N. Chowdhury, R. Raje
In recent years, the number of smart mobile devices has rapidly increased worldwide. This explosion of continuously connected mobile devices has resulted in an exponential growth in the number of publically available mobile Apps. To facilitate the selection of mobile Apps, from various available choices, the App distribution platforms typically rank/recommend Apps based on average star ratings, the number of downloads, and associated reviews - the external aspect of an App. However, these ranking schemes typically tend to ignore critical internal aspects (e.g., security vulnerabilities) of the Apps. Such an omission of internal aspects is certainly not desirable, especially when many of the users do not possess the necessary skills to evaluate the internal aspects and choose an App based on the default ranking scheme which uses the external aspect. In this paper, we build upon our earlier efforts by focusing specifically on the security-related internal aspect of an App and its combination with the external aspect computed from the user reviews by identifying security-related comments.We use this combination to rank-order similar Apps. We evaluate our approach on publicly available Apps from the Google PlayStore and compare our ranking with prevalent ranking techniques such as the average star ratings. The experimental results indicate the effectiveness of our proposed approach.
{"title":"SERS: A Security-Related and Evidence-Based Ranking Scheme for Mobile Apps","authors":"N. Chowdhury, R. Raje","doi":"10.1109/TPS-ISA48467.2019.00024","DOIUrl":"https://doi.org/10.1109/TPS-ISA48467.2019.00024","url":null,"abstract":"In recent years, the number of smart mobile devices has rapidly increased worldwide. This explosion of continuously connected mobile devices has resulted in an exponential growth in the number of publically available mobile Apps. To facilitate the selection of mobile Apps, from various available choices, the App distribution platforms typically rank/recommend Apps based on average star ratings, the number of downloads, and associated reviews - the external aspect of an App. However, these ranking schemes typically tend to ignore critical internal aspects (e.g., security vulnerabilities) of the Apps. Such an omission of internal aspects is certainly not desirable, especially when many of the users do not possess the necessary skills to evaluate the internal aspects and choose an App based on the default ranking scheme which uses the external aspect. In this paper, we build upon our earlier efforts by focusing specifically on the security-related internal aspect of an App and its combination with the external aspect computed from the user reviews by identifying security-related comments.We use this combination to rank-order similar Apps. We evaluate our approach on publicly available Apps from the Google PlayStore and compare our ranking with prevalent ranking techniques such as the average star ratings. The experimental results indicate the effectiveness of our proposed approach.","PeriodicalId":129820,"journal":{"name":"2019 First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126899370","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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