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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 10
Issue Published : 109
Article Submitted : 16130
Article Published : 7097
Total Authors : 19084
Total Reviewer : 688
Total Countries : 125
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| Paper Title: | Detection of phishing websites by using ing Machine Learning- Based URL analysis |
| Authors Name: | Bhavyasree v.k , Dr.G Chithra Ganapathi , MR. S Dinesh Kumar |
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| Author Reg. ID: |
IJRTI_183291
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| Published Paper Id: | IJRTI2207237 |
| Published In: | Volume 7 Issue 7, July-2022 |
| DOI: | |
| Abstract: | In recent years, with the increasing use of mobile devices, there is a growing trend to move almost all real-world operations to the cyber world. Although this makes easy our daily lives, it also brings many security breaches due to the anonymous structure of the Internet. Used antivirus programs and firewall systems can prevent most of the attacks. However, experienced attackers target on the weakness of the computer users by trying to phish them with bogus web pages. These pages imitate some popular banking, social media, e-commerce, etc. sites to steal some sensitive information such as, user-ids, passwords, bank account, credit card numbers, etc. Phishing detection is a challenging problem, and many different solutions are proposed in the market as a blacklist, rule- based detection, anomaly-based detection, etc. In the literature, it is seen that current works tend on the use of machine learning-based anomaly detection due to its dynamic structure, especially for catching the “zero-day” attacks. In this paper, we proposed a machine learning-based phishing detection system by using eight different algorithms to analyze the URLs, and three different datasets to compare the results with other works. The experimental results depict that the proposed models have an outstanding performance with a success rate. |
| Keywords: | detection of phishing websites by using machine learning |
| Cite Article: | "Detection of phishing websites by using ing Machine Learning- Based URL analysis", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 7, page no.1509 - 1527, July-2022, Available :http://www.ijrti.org/papers/IJRTI2207237.pdf |
| Downloads: | 000204877 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2207237
Registration ID:183291
Published In: Volume 7 Issue 7, July-2022
DOI (Digital Object Identifier):
Page No: 1509 - 1527 Country: calicut, kerala, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2207237 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2207237 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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