CFP last date
22 April 2024
Reseach Article

Analysis of Machine Learning Algorithms to Protect from Phishing in Web Data Mining

by N. Swapna Goud
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 159 - Number 1
Year of Publication: 2017
Authors: N. Swapna Goud
10.5120/ijca2017912743

N. Swapna Goud . Analysis of Machine Learning Algorithms to Protect from Phishing in Web Data Mining. International Journal of Computer Applications. 159, 1 ( Feb 2017), 30-34. DOI=10.5120/ijca2017912743

@article{ 10.5120/ijca2017912743,
author = { N. Swapna Goud },
title = { Analysis of Machine Learning Algorithms to Protect from Phishing in Web Data Mining },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2017 },
volume = { 159 },
number = { 1 },
month = { Feb },
year = { 2017 },
issn = { 0975-8887 },
pages = { 30-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume159/number1/26969-2017912743/ },
doi = { 10.5120/ijca2017912743 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:04:36.602225+05:30
%A N. Swapna Goud
%T Analysis of Machine Learning Algorithms to Protect from Phishing in Web Data Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 159
%N 1
%P 30-34
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The term Big data is a large data sets those outgrow the simple kind of database and data handling design. We designed prototype of website phishing detection solution to address the requirements for both effective and efficient phishing detection machine learning big data allows us to dig into a tremendous amount of data that fix the problem and extract predictive signals for the phishing problem. As the cyber security problems grows many types of phishing activities may arises bid data analytics is pretty helpful in identifying various phishing threats of suppliers by scanning various data roots such as personal contacts service level agreements exploring various unstructured data sources log reports and big data analysis highly suitable for analyzing. Our research work presents big data analytics that aims to prevent malicious email notifications & phishing from web service

References
  1. Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules in large databases. In Proc. international conference on very large databases (pp. 478–499). Morgan Kaufmann, Los Altos, CA: Santiage, Chile.
  2. 2 Anti-Phishing Working Group (2007). Phishing Activity Trends Report. Available from http://antiphishing.org/reports/apwg_report_sep2007_final.pdf.
  3. S. Kumar and E.H. Spafford, “A Pattern Matching Model forMisuse Intrusion Detection,” Proc. 17th Nat’l Computer Security Conf., pp. 11-21, Oct. 1994.
  4. C. Chin, A. Ray, and V. Rajagopalan, “Symbolic Time Series Analysis for Anomaly Detection: A Comparative Evaluation,” Signal Processing, vol. 85, no. 9, pp. 1859-1868, Sept. 2005.
  5. M. Thottan and C. Ji, “Anomaly Detection in IP Networks,” IEEE Trans. Signal Processing, vol. 51, no. 8, pp. 2191-2204, 2003.
  6. C. Kruegel and G. Vigna, “Anomaly Detection of Web-Based Attacks,” Proc. ACM Conf. Computer and Comm. Security, Oct. 2003.
  7. Z. Zhang, J. Li, C.N. Manikopoulos, J. Jorgenson, and J. Ucles, “HIDE: A Hierarchical Network Intrusion Detection System Using Statistical Preprocessing and Neural Network Classification,”Proc. 2001 IEEE Workshop Information Assurance, pp. 85-90, June 2001.
  8. S.T. Sarasamma, Q.A. Zhu, and J. Huff, “Hierarchical Kohonen Net for Anomaly Detection in Network Security,” IEEE Trans. Systems, Man, and Cybernetics-Part B, vol. 35, no. 2, Apr. 2005.
  9. J. Gomez and D.D. Gupta, “Evolving Fuzzy Classifiers for Intrusion Detection,” Proc. 2002 IEEE Workshop Information Assurance, June 2001.
Index Terms

Computer Science
Information Sciences

Keywords

Phishing Cybercrime Big Data Webservice Emails