Call for Paper - January 2020 Edition
IJCA solicits original research papers for the January 2020 Edition. Last date of manuscript submission is December 20, 2019. Read More

Anti-phishing using Big Data

Print
PDF
IJCA Proceedings on International Conference on Emerging Trends in Computing and Communication
© 2018 by IJCA Journal
ICETCC 2017 - Number 3
Year of Publication: 2018
Authors:
Vanita Khamkar
Payal Ingale
Dhanraj Walunj

Vanita Khamkar, Payal Ingale and Dhanraj Walunj. Article: Anti-phishing using Big Data. IJCA Proceedings on International Conference on Emerging Trends in Computing and Communication ICETCC 2017(3):5-7, June 2018. Full text available. BibTeX

@article{key:article,
	author = {Vanita Khamkar and Payal Ingale and Dhanraj Walunj},
	title = {Article: Anti-phishing using Big Data},
	journal = {IJCA Proceedings on International Conference on Emerging Trends in Computing and Communication},
	year = {2018},
	volume = {ICETCC 2017},
	number = {3},
	pages = {5-7},
	month = {June},
	note = {Full text available}
}

Abstract

Now a day's phishing attack has become one of the most serious issues faced by internet users, organizations and service providers. In phishing attack attacker tries to obtain the personal information of the users by using spoofed emails or by using fake websites or both. The internet community is still looking for the complete solution to secure the internet from such attacks. The users will be victim for this kind of activities, because phishing web pages looks very similar to real ones, so finds difficult to distinguish between the fake website and ones, detecting this kind of webpage is very difficult because for identification it takes several attributes into consideration which user might not knowing those things. The existing phishing detection systems are highly dependent on database and they are very time consuming also. In this proposed system, Hadoop-Map Reduce is used for fast retrieval of URL attributes, which plays a key role in identifying phishing web pages and it is known for its time efficiency and throughput also can gained using this.

References

  • Fu, A. Y. ; Liu Wenyin; Xiaotie Deng, "Detecting Phishing Web Pages with Visual Similarity Assessment Based on Earth Mover's Distance (EMD)," Dependable and Secure Computing, IEEE Transactions on , vol. 3, no. 4, pp. 301,311, Oct. -Dec. 2006.
  • Hong Bo; Wang Wei; Wang Liming; Geng Guanggang; Xiao Yali; Li Xiaodong; Mao Wei, "A Hybrid System to Find & Fight Phishing Attacks Actively," Web Intelligence and Intelligent Agent Technology (WI-IAT), 2011 IEEE/WIC/ACM International Conference on vol. 1, no. , pp. 506,509, 22-27 Aug. 2011.
  • J. Dean and S. Ghemawat, "MapReduce: simplified data processing on large clusters," Communications of the ACM - 50th anniversary issue: 1958 - 2008, vol. 51, no. 1, pp. 107-113, 2008.
  • Aburrous, M. ; Hossain, M. A. ; Dahal, K. ; Thabatah, F. , "Modelling Intelligent Phishing Detection System for E-banking Using Fuzzy Data Mining," CyberWorlds, 2009. CW '09. International Conference on, vol. , no. , pp. 265,272, 7-11 Sept. 2009.
  • Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten (2009); The WEKA Data Mining Software: An Update; SIGKDD Explorations, Volume 11, Issue 1.
  • Tanenbaum, A. S. (2010). Computer Networks (5th Edition). Prentice Hall; 5 edition (October 7, 2010).