Electronic Product Code Discovery Service (EPCDS) is an important concept in supply chain processes and in Internet of Things (IOT). It allows supply chain participants to search for their partners, communicate with t...Electronic Product Code Discovery Service (EPCDS) is an important concept in supply chain processes and in Internet of Things (IOT). It allows supply chain participants to search for their partners, communicate with them and share product information using standardized interfaces securely. Many researchers have been proposing different EPCDS models, considering different requirements. In this paper, we describe existing architecture designs of EPCDS systems, namely Directory Service Model, Query Relay Model and Aggregating Discovery Service Model (ADS). We also briefly mention Secure Discovery Service (SecDS) Model, which is an improved version of Directory Service Model with a secure attribute-based access control mechanism. Then, we analyze the strengths and limitations of these models, by comparing based on non-functional features such as data ownership, confidentiality, business relationship independence, availability, reliability, implementation complexity, visibility, and scalability. From the analysis results, we have a better understanding of which model is more suitable in what kinds of situations or scenarios. Moreover, we suggest possible improvements and identify possible future add-on applications to SecDS model in the paper.展开更多
Wearing smartwatches becomes increasingly popular in people’s lives.This paper shows that a smartwatch can help its bearer authenticate to a login system effectively and securely even if the bearer’s password has al...Wearing smartwatches becomes increasingly popular in people’s lives.This paper shows that a smartwatch can help its bearer authenticate to a login system effectively and securely even if the bearer’s password has already been revealed.This idea is motivated by our observation that a sensor-rich smartwatch is capable of tracking the wrist motions of its bearer typing a password or PIN,which can be used as an authentication factor.The major challenge in this research is that a sophisticated attacker may imitate a user’s typing behavior as shown in previous research on keystroke dynamics based user authentication.We address this challenge by applying a set of machine learning and deep learning classifiers on the user’s wrist motion data that are collected from a smartwatch worn by the user when inputting his/her password or PIN.Our solution is user-friendly since it does not require users to perform any additional actions when typing passwords or PINs other than wearing smartwatches.We conduct a user study involving 51 participants so as to evaluate the feasibility and performance of our solution.User study results show that the best classifier is the Bagged Decision Trees,which yields 4.58% FRR and 0.12% FAR on a QWERTY keyboard,and 6.13% FRR and 0.16% FAR on a numeric keypad.展开更多
文摘Electronic Product Code Discovery Service (EPCDS) is an important concept in supply chain processes and in Internet of Things (IOT). It allows supply chain participants to search for their partners, communicate with them and share product information using standardized interfaces securely. Many researchers have been proposing different EPCDS models, considering different requirements. In this paper, we describe existing architecture designs of EPCDS systems, namely Directory Service Model, Query Relay Model and Aggregating Discovery Service Model (ADS). We also briefly mention Secure Discovery Service (SecDS) Model, which is an improved version of Directory Service Model with a secure attribute-based access control mechanism. Then, we analyze the strengths and limitations of these models, by comparing based on non-functional features such as data ownership, confidentiality, business relationship independence, availability, reliability, implementation complexity, visibility, and scalability. From the analysis results, we have a better understanding of which model is more suitable in what kinds of situations or scenarios. Moreover, we suggest possible improvements and identify possible future add-on applications to SecDS model in the paper.
基金partially supported by the Singapore National Research Foundation under NCR Award Number NRF2015NCR-NCR003-002the funding body is in all the parts of the paper+1 种基金supported by the National Key Research and Development Program of China under Grant 2016YFB0800500supported by AXA Research Fund.
文摘Wearing smartwatches becomes increasingly popular in people’s lives.This paper shows that a smartwatch can help its bearer authenticate to a login system effectively and securely even if the bearer’s password has already been revealed.This idea is motivated by our observation that a sensor-rich smartwatch is capable of tracking the wrist motions of its bearer typing a password or PIN,which can be used as an authentication factor.The major challenge in this research is that a sophisticated attacker may imitate a user’s typing behavior as shown in previous research on keystroke dynamics based user authentication.We address this challenge by applying a set of machine learning and deep learning classifiers on the user’s wrist motion data that are collected from a smartwatch worn by the user when inputting his/her password or PIN.Our solution is user-friendly since it does not require users to perform any additional actions when typing passwords or PINs other than wearing smartwatches.We conduct a user study involving 51 participants so as to evaluate the feasibility and performance of our solution.User study results show that the best classifier is the Bagged Decision Trees,which yields 4.58% FRR and 0.12% FAR on a QWERTY keyboard,and 6.13% FRR and 0.16% FAR on a numeric keypad.
