Document Fraud Detection
DOI:
https://doi.org//10.32628/CSEIT1951135Keywords:
Document Fraud Detection, Frequency Analysis, Encryption ad Decryption.Abstract
In today scenario for data and fund transfer we are mainly depended on the internet. So prevention of fraud, abuse and data alteration through internet has become a major concern of many organizations. Our paper focuses the direction towards the document fraud detection. In this direction we proposed an efficient approach where we send the encrypted data via internet and maintain the log table of the data sends. The log table contains the information about individual word and numeric value along with the position and count. If any attacker attacks the data for updation or any violation again a log table is created based on the word as well as the position of the word and count. Our algorithm check the alteration based on the position and the frequency count. If any mismatch is detected server alerts the client about the document attack and resend the document to the client. The above scenario is explained by the result analysis which shows the effectiveness of the approach.
References
- Elkan, C. (2001). Magical Thinking in Data Mining: Lessons from COIL Challenge 2000. Proc. of SIGKDD01, 426-431.
- Kai Li, Peng Li, " A Selective Fuzzy Clustering Ensemble Algorithm " , International Journal of Advanced Computer Research (IJACR), Volume-3, Issue-13, December-2013 ,pp.1-6.
- Ashutosh Kumar Dubey, Animesh Kumar Dubey, Vipul Agarwal, Yogeshver Khandagre,
- “Knowledge Discovery with a Subset-Superset Approach for Mining Heterogeneous Data with Dynamic Support”,Conseg-2012.
- Preeti Khare, Hitesh Gupta, “Finding Frequent Pattern with Transaction and Occurrences based on Density Minimum Support Distribution”,
- IEEE 2011. Dr. Bhavani Thuraisingham,” Data Mining for Malicious Code Detection and Security Applications”, EuropeanIntelligence and
- Security Informatics Conference, 2011.
- Sherly K.K,” A Comparative Assessment Of Supervised Data Mining Techniques For Fraud Prevention”, TIST. Int.J. Sci.Tech.Res., Vol.1 (2012), 1-6.
- Clifton Phua, Kate Smith-Miles,Vincent Cheng- Siong Lee, And Ross Gayler,” Resilient Identity Crime Detection”, IEEE Transactions On
- Knowledge And Data Engineering, Vol. 24, No. 3, March 2012.
- Syed Imran Ahmed Qadri, Kiran Pandey, “Tag Based Client Side Detection of Content Sniffing Attacks with File Encryption and File Splitter
- Technique”, International Journal of Advanced Computer Research (IJACR), Volume-2, Number-3, Issue-5, September-2012.
- V.Priyadharshini, G.Adiline Macriga,” An Efficient Data Mining for Credit Card Fraud Detection using Finger Print Recognition”,
- International Journal of Advanced Computer Research (IJACR),Volume-2 Number-4 Issue-7 December-2012.
- Shashi Sharma, Ram Lal Yadav, “Comparative Study of K-means and Robust Clustering” , International Journal of Advanced Computer
- Research (IJACR), Volume-3, Issue-12, September-2013 ,pp.207-210.
- Namrata Shukla, Shweta Pandey,” Document
- Fraud Detection with the help of Data Mining and Secure Substitution Method with Frequency
- Analysis”, International Journal of Advanced Computer Research (IJACR) ,Volume 2 Number 2, June 2012.
- Animesh Dubey, Ravindra Gupta, Gajendra
- Singh Chandel, “An Efficient Partition Technique to reduce the Attack Detection Time with Web based Text and PDF files”, (IJACR),Volume-3 Number-1 Issue-9 March-2013.
Downloads
Published
Issue
Section
License
Copyright (c) IJSRCSEIT
This work is licensed under a Creative Commons Attribution 4.0 International License.