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Karthi, M.
- N Level Unicode Position Character Length Cipher for Securing Smart Card
Abstract Views :184 |
PDF Views:3
Authors
Affiliations
1 Department of EEE, Karpagam College of Engineering, Coimbatore, IN
2 Department of IT, Velalar College of Engineering and Technology, Erode, IN
3 Sri Sivasubramaniya Nadar College of Engineering, Chennai, IN
4 Velalar College of Engineering and Technology, Erode, IN
5 Velalar College of Engineering and Technology, Erode, ID
1 Department of EEE, Karpagam College of Engineering, Coimbatore, IN
2 Department of IT, Velalar College of Engineering and Technology, Erode, IN
3 Sri Sivasubramaniya Nadar College of Engineering, Chennai, IN
4 Velalar College of Engineering and Technology, Erode, IN
5 Velalar College of Engineering and Technology, Erode, ID
Source
Networking and Communication Engineering, Vol 2, No 7 (2010), Pagination: 153-158Abstract
In day to day life, Smart card plays an important role. Open your wallet! See at least 20 to 25 cards in it. Wondering?? Today, the world is fully in the hands of smart cards since it is easy to carry and even if it is stolen, it is secure. But due to the increase in hackers today, Smart cards are becoming insecure now. Even the Encrypted PIN can be easily stolen by the cryptanalysts. So it is time now to secure the Smart card. In our previous paper [1], we have considered all the vulnerabilities of the Smart card and have provided various ways to overcome it. Still we feel that my algorithm is suffering from forgery attack. In this paper, we have applied a new encryption and a new hash algorithm to keep the information secure in Smart cards and to overcome forgery attack. The main advantage of this paper is, on an average, even the supercomputer will take 101084 years to decrypt, which is 42.2% higher than the previous proposals.
Keywords
Cryptographic Algorithms, Low Cost Smart Cards, Smart Cards, Brand New Hash.
- A Novel Technique for Multi-Class Ordinal Regression-APDC
Abstract Views :208 |
PDF Views:0
Authors
Affiliations
1 Sathyabama University, Rajiv Gandhi Road, Jeppiaar Nagar, Chennai - 600119, Tamil Nadu, IN
2 Karapagam College of Engineering, Myleripalayam Village, Othakkal Mandapam Post, Coimbatore - 641032, Tamil Nadu, IN
3 Madanapalle Institute of Technology and Science, P.B. No:14, Kadiri Road, Angallu Village, Chittoor District, Madanapalle - 517325, Andhra Pradesh, IN
1 Sathyabama University, Rajiv Gandhi Road, Jeppiaar Nagar, Chennai - 600119, Tamil Nadu, IN
2 Karapagam College of Engineering, Myleripalayam Village, Othakkal Mandapam Post, Coimbatore - 641032, Tamil Nadu, IN
3 Madanapalle Institute of Technology and Science, P.B. No:14, Kadiri Road, Angallu Village, Chittoor District, Madanapalle - 517325, Andhra Pradesh, IN
Source
Indian Journal of Science and Technology, Vol 9, No 10 (2016), Pagination:Abstract
Objectives: Ordinal regression is one which is used in Multiclass classification where there is an essential ordering among the classes. The training dataset is initially classified depending on the Random threshold values θ. Based on these values, the distance between the different class labels are predicted by one against one technique. Method: All Pairs Distance Calculation using one against one technique [APDC_1 AG 1] is Proposed to validate the work. But in the referred previous work, distance is calculated using adjacent classes, but here all pairs distance calculation is used to find the class label distance to all class label pairs. Findings: On the whole, New trained data are in the form of one dimensional representation. Here, with the knowledge of proposed work, testing data is tested with New trained data set and the results are produced. The Proposed method is seen to be ambitious when compared with previous work. Beside this, an additional set of experiments is done to study the potential quantifiability and illustratability of the proposed method when using APDC as base methodology. Improvements: Proposed work is analyzed with Kernel discriminant analysis, Logistic Regression, Classification via Regression, Multiclass Classifier and found APDC has attained better results according to all measures.Keywords
All Pairs Distance Calculations, Hyper Line, Latent Space Representation, Multi-Class Ordinal Regression, One Against One Method, Ordinal Classification, ProjectionFull Text
- Usage of Bioinformatic Data for Remote Authentication in Wireless Networks
Abstract Views :187 |
PDF Views:0
Authors
R. Priscilla
1,
M. Karthi
1
Affiliations
1 Department of Information Technology, St. Joseph’s Institute of Technology, Chennai, IN
1 Department of Information Technology, St. Joseph’s Institute of Technology, Chennai, IN
Source
ICTACT Journal on Image and Video Processing, Vol 9, No 1 (2018), Pagination: 1833-1837Abstract
Authentication is the step to approve the correctness of an attribute of a individual or entity group. Sensitive information might help in making the authentication. Regularly this encrypted information is processed via wireless network and which need remote authentication for information access process. In the proposed work, a robust authentication technique is performed, which is based on segmentation, symmetric encryption and data hiding. If a user wants to be remotely authenticated, initially user has to select a video. The user’s biometric signal is encrypted using a symmetric encryption method. After encrypted information is vectorized the information hiding process is accomplish using Qualified Significant Wavelet Trees (QSWTs). QSWT is effectively achieve the invisibility and resistance during attacks and stability in data hidden process. Also, the Inverse Discrete Wavelet Transform (IDWT) is applied to extract the hiding data from the stego-object subsequently an appropriate decryption process to recover the biometric image. Experimental results are stated that the proposed method would turnout security virtue and robustness. Triple DES technique is used in the proposed work. This is the technique that is used to encrypt the biometric data into a scrambled format which is difficult to understand by the attackers. It is a very useful and efficient method of encryption because of its tendency to use less data for performing its services.Keywords
Railway Accident, Decision Making, D3 Algorithm.References
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- L. Mary Glandence, M. Karthi and V. Maria Anu, “A Statistical Comparison of Logistic Regression and different Bayes Classification Methods for Machine Learning”, ARPN Journal of Engineering and Applied Sciences, Vol. 10, No. 14, pp. 1-14, 2015.
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- L. Mary Gladence, M. Karthi and T. Ravi, “A Novel Technique for Multi-Class Ordinal Regression-APDC”, Indian Journal of Science and Technology, Vol. 9, No. 10, pp. 1-8, 2016.