The week's pick
Reseach Article
Analysis of various Machine Learning Techniques to Detect Phishing Email
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 178 - Number 38 |
| Year of Publication: 2019 |
| Authors: Meenu, Sunila Godara |
10.5120/ijca2019919251
|
Meenu, Sunila Godara . Analysis of various Machine Learning Techniques to Detect Phishing Email. International Journal of Computer Applications. 178, 38 ( Aug 2019), 4-12. DOI=10.5120/ijca2019919251
Abstract
Spamming is the method for mishandling an electronic informing framework by sending spontaneous mass messages. This issue makes clients doubt email frameworks. Phishing or spam is an extortion method utilized for wholesale fraud where clients get phony messages from misdirecting tends to that appear as having a place with an honest to goodness and genuine business trying to take individual points of interest. To battle against spamming, a cloud-based framework Microsoft azure and uses prescient investigation with machine making sense of how to manufacture confidence in personalities. The goal of this paper is to construct a spam channel utilizing various machine learning techniques. Classification is a machine learning strategy uses that can be viably used to recognize spam, builds and tests models, utilizing diverse blends of settings, and compare various machine learning technique, and measure the accuracy of a trained model and computes a set of evaluation metrics.
References
- Almomani, Ammar, B. B. Gupta, SamerAtawneh, A. Meulenberg, and Eman Almomani. exercises 15, no. 4 pp. 2070-2090,2013 "A review of phishing email separating procedures." IEEE correspondences overviews and instructional .
- Gansterer, W. N., and Pölz, D., pp. 449-460,2009 “Email characterization for phishing protection.” In Advances in Information Retrieval,.Springer Berlin Heidelberg .
- McGregor, Anthony, Mark Hall, Perry Lorier, and James Brunskill. pp. 205-214, 2004 "Stream bunching utilizing machine learning strategies." In International Workshop on Passive and Active Network Measurement ,Springer, Berlin, Heidelberg, .
- Read, Jonathon. , pp. 43-48, 2005 "Utilizing emojis to lessen reliance in machine learning methods for slant characterization." In Proceedings of the ACL understudy investigate workshop,Relationship for Computational Linguistics.
- Kotsiantis, Sotiris B., I. Zaharakis, and P. Pintelas. building 160 pp 3-24.,2007, "Regulated machine taking in: An audit of arrangement strategies." Emerging man-made reasoning applications in PC.
- Rathi, M., & Pareek, V. 2013 “Spam Mail Detection through Data Mining-A Comparative Performance Analysis”. International Journal of Modern Education and Computer Science,(12).
- Abu-Nimeh, Saeed, Dario Nappa, Xinlei Wang, and Suku Nair. , pp. 60-69 , 2007 "An examination of machine learning procedures for phishing identification." In Proceedings of the counter phishing working gatherings second yearly eCrime analysts summit.
- Sommer, Robin, and Vern Paxson. , pp. 305-316 , 2010 "Outside the shut world: On utilizing machine learning for arrange interruption location." IEEE.
- Kolari, Pranam, Akshay Java, Tim Finin, Tim Oates, and Anupam Joshi. vol. 6, pp. 1351-1356. 2006 "Distinguishing spam writes: A machine learning approach." In AAAI.
- Crawford, Michael, Taghi M. Khoshgoftaar, Joseph D. Prusa, Aaron N. Richter, and Hamzah Al Najada. no. 1: 23,2015 "Overview of audit spam location utilizing machine learning systems." Journal of Big Data .
- Wang, Alex Hai. , pp. 335-342, 2010 "Identifying spam bots in online long range interpersonal communication locales: a machine learning approach." In IFIP Annual Conference on Data and Applications Security and Privacy,. Springer, Berlin, Heidelberg.
- Castillo, Carlos, Debora Donato, Aristides Gionis, Vanessa Murdock, and FabrizioSilvestri. pp. 423-430, 2007 "Know your neighbors: Web spam discovery utilizing the web topology." In Proceedings of the 30th yearly worldwide ACM SIGIR gathering on Research and advancement in data recovery.
- Benevenuto, Fabricio, Gabriel Magno, Tiago Rodrigues, and Virgilio Almeida. vol. 6, pp. 12, 2010 "Recognizing spammers on twitter." In Collaboration, electronic informing, hostile to manhandle and spam meeting (CEAS).
- Sasaki, Minoru, and Hiroyuki Shinnou. Vol. 4 , 2005 "Spam location utilizing content bunching." In Cyberworlds,2005.worldwide meeting , IEEE .
- Garera, Sujata, Niels Provos, Monica Chew, and Aviel D. Rubin. malcode, pp. 1-8, 2007 "A structure for discovery and estimation of phishing assaults." In Proceedings of the ACM workshop on Recurring .
- Pang, Bo, Lillian Lee, and Shivakumar Vaithyanathan. Vol.10, pp. 79-86, 2002 "Thumbs up: slant arrangement utilizing machine learning systems." In Proceedings of the ACL-02 meeting on Empirical techniques in normal dialect preparing.
- Witten, Ian H., Eibe Frank, Mark A. Lobby, and Christopher J. Buddy. 2016. ” Information Mining: Practical machine learning devices and systems.”
- Kumar, R. K., Poonkuzhali, G., and Sudhakar, P. Vol. 1, pp. 14-16,march-2012 “Similar investigation on email spam classifier utilizing information mining procedures”. In Proceedings of the International Multi Conference of Engineers and Computer Scientist.
- Jyoti Chhikara, CSE Dept, PDMCEW India: Volume 3, Issue 5, May 2013.” International Journal of Advanced Research in Computer Science and Software Engineering”
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