Biblio

Filters: Author is Tang, C.  [Clear All Filters]
2021-03-29
Tang, C., Fu, X., Tang, P..  2020.  Policy-Based Network Access and Behavior Control Management. 2020 IEEE 20th International Conference on Communication Technology (ICCT). :1102—1106.

Aiming at the requirements of network access control, illegal outreach control, identity authentication, security monitoring and application system access control of information network, an integrated network access and behavior control model based on security policy is established. In this model, the network access and behavior management control process is implemented through abstract policy configuration, network device and application server, so that management has device-independent abstraction, and management simplification, flexibility and automation are improved. On this basis, a general framework of policy-based access and behavior management control is established. Finally, an example is given to illustrate the method of device connection, data drive and fusion based on policy-based network access and behavior management control.

2020-11-04
Stange, M., Tang, C., Tucker, C., Servine, C., Geissler, M..  2019.  Cybersecurity Associate Degree Program Curriculum. 2019 IEEE International Symposium on Technologies for Homeland Security (HST). :1—5.

The spotlight is on cybersecurity education programs to develop a qualified cybersecurity workforce to meet the demand of the professional field. The ACM CCECC (Committee for Computing Education in Community Colleges) is leading the creation of a set of guidelines for associate degree cybersecurity programs called Cyber2yr, formerly known as CSEC2Y. A task force of community college educators have created a student competency focused curriculum that will serve as a global cybersecurity guide for applied (AAS) and transfer (AS) degree programs to develop a knowledgeable and capable associate level cybersecurity workforce. Based on the importance of the Cyber2yr work; ABET a nonprofit, non-governmental agency that accredits computing programs has created accreditation criteria for two-year cybersecurity programs.

2018-05-30
Liu, Y., Li, R., Liu, X., Wang, J., Tang, C., Kang, H..  2017.  Enhancing Anonymity of Bitcoin Based on Ring Signature Algorithm. 2017 13th International Conference on Computational Intelligence and Security (CIS). :317–321.

Bitcoin is a decentralized digital currency, widely used for its perceived anonymity property, and has surged in popularity in recent years. Bitcoin publishes the complete transaction history in a public ledger, under pseudonyms of users. This is an alternative way to prevent double-spending attack instead of central authority. Therefore, if pseudonyms of users are attached to their identities in real world, the anonymity of Bitcoin will be a serious vulnerability. It is necessary to enhance anonymity of Bitcoin by a coin mixing service or other modifications in Bitcoin protocol. But in a coin mixing service, the relationship among input and output addresses is not hidden from the mixing service provider. So the mixing server still has the ability to track the transaction records of Bitcoin users. To solve this problem, We present a new coin mixing scheme to ensure that the relationship between input and output addresses of any users is invisible for the mixing server. We make use of a ring signature algorithm to ensure that the mixing server can't distinguish specific transaction from all these addresses. The ring signature ensures that a signature is signed by one of its users in the ring and doesn't leak any information about who signed it. Furthermore, the scheme is fully compatible with existing Bitcoin protocol and easily to scale for large amount of users.

2018-04-04
Zhang, B., Ye, J., Feng, C., Tang, C..  2017.  S2F: Discover Hard-to-Reach Vulnerabilities by Semi-Symbolic Fuzz Testing. 2017 13th International Conference on Computational Intelligence and Security (CIS). :548–552.
Fuzz testing is a popular program testing technique. However, it is difficult to find hard-to-reach vulnerabilities that are nested with complex branches. In this paper, we propose semi-symbolic fuzz testing to discover hard-to-reach vulnerabilities. Our method groups inputs into high frequency and low frequency ones. Then symbolic execution is utilized to solve only uncovered branches to mitigate the path explosion problem. Especially, in order to play the advantages of fuzz testing, our method locates critical branch for each low frequency input and corrects the generated test cases to comfort the branch condition. We also implemented a prototype\textbackslashtextbarS2F, and the experimental results show that S2F can gain 17.70% coverage performance and discover more hard-to-reach vulnerabilities than other vulnerability detection tools for our benchmark.