Visible to the public Biblio

Filters: Author is Shi, T.  [Clear All Filters]
2019-09-26
Li, S., Wang, F., Shi, T., Kuang, J..  2019.  Probably Secure Multi-User Multi-Keyword Searchable Encryption Scheme in Cloud Storage. 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). :1368-1372.
Searchable encryption server protects privacal data of data owner from leaks. This paper analyzes the security of a multi-user searchable encryption scheme and points out that this scheme does not satisfy the invisibility of trapdoors. In order to improve the security of the original scheme, this paper proposes a probably secure multi-user multi-keyword searchable encryption scheme. New secheme not only ensures the confidentiality of the cipher text keyword, but also does not increase the encryption workload of the data owner when the new data user joins. In the random oracle model, based on the hard problem of decisional Diffie-Hellman, it is proved that the scheme has trapdoor indistinguishability. In the end, obtained by the simulation program to achieve a new computationally efficient communication at low cost.
2019-01-16
Shi, T., Shi, W., Wang, C., Wang, Z..  2018.  Compressed Sensing based Intrusion Detection System for Hybrid Wireless Mesh Networks. 2018 International Conference on Computing, Networking and Communications (ICNC). :11–15.
As wireless mesh networks (WMNs) develop rapidly, security issue becomes increasingly important. Intrusion Detection System (IDS) is one of the crucial ways to detect attacks. However, IDS in wireless networks including WMNs brings high detection overhead, which degrades network performance. In this paper, we apply compressed sensing (CS) theory to IDS and propose a CS based IDS for hybrid WMNs. Since CS can reconstruct a sparse signal with compressive sampling, we process the detected data and construct sparse original signals. Through reconstruction algorithm, the compressive sampled data can be reconstructed and used for detecting intrusions, which reduces the detection overhead. We also propose Active State Metric (ASM) as an attack metric for recognizing attacks, which measures the activity in PHY layer and energy consumption of each node. Through intensive simulations, the results show that under 50% attack density, our proposed IDS can ensure 95% detection rate while reducing about 40% detection overhead on average.