A Tamper Detection Method for RFID Tag Data

Author(s):  
Akira Yamamoto ◽  
Shigeya Suzuki ◽  
Hisakazu Hada ◽  
Jin Mitsugi ◽  
Fumio Teraoka ◽  
...  
2014 ◽  
Vol 513-517 ◽  
pp. 1005-1008
Author(s):  
Xiao Ming Yao ◽  
Hong Yu Chen ◽  
Hong Lei Li ◽  
Xiao Yi Zhou

Data tampering as one of the primary security issues in RFID-enabled applications has been presented in recent years and proposals based on watermarking have been put forward to address different aspects of tampering in RFID tags. However, most of current researches are focused on the way of generating the watermark from the data to be protected and embedding it into the tag field (usually the field of serial number or SN) used as the cover medium, thus the innate structural coding relationship as a new clue to guess out the hidden watermark might be ignored. In this paper, this flaw has been fully considered, and a novel tamper detection method using CFB based encryption to hide the location clues is presented. Although it cant resist the attack from statistical analysis either, theoretical analysis has demonstrated that our scheme outperforms its previous counterparts in data security.


2021 ◽  
Vol 1750 ◽  
pp. 012071
Author(s):  
Rui Wang ◽  
Qiujing Gong ◽  
Ke Zhen ◽  
Xing Zhe Hou ◽  
Min He ◽  
...  

Author(s):  
K. Pegg-Feige ◽  
F. W. Doane

Immunoelectron microscopy (IEM) applied to rapid virus diagnosis offers a more sensitive detection method than direct electron microscopy (DEM), and can also be used to serotype viruses. One of several IEM techniques is that introduced by Derrick in 1972, in which antiviral antibody is attached to the support film of an EM specimen grid. Originally developed for plant viruses, it has recently been applied to several animal viruses, especially rotaviruses. We have investigated the use of this solid phase IEM technique (SPIEM) in detecting and identifying enteroviruses (in the form of crude cell culture isolates), and have compared it with a modified “SPIEM-SPA” method in which grids are coated with protein A from Staphylococcus aureus prior to exposure to antiserum.


2015 ◽  
Vol 6 (4) ◽  
pp. 171-184
Author(s):  
Liangbo Xie ◽  
Jiaxin Liu ◽  
Yao Wang ◽  
Chuan Yin ◽  
Guangjun Wen

Author(s):  
Weihai Sun ◽  
Lemei Han

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.


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