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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Detection of Phishing Websites Using Machine Learning |
| Authors Name: | Sugat Ingle , Prince Gupta , Shreyash Bhole , Kamlesh Janawale |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_209355
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| Published Paper Id: | IJNRD2403190 |
| Published In: | Volume 9 Issue 3, March-2024 |
| DOI: | |
| Abstract: | The detection of phishing websites and online content yields various indicators. One prevalent form of successful cybercrime involves phishing sites that lure users to deceptive websites mimicking legitimate ones, aiming to illicitly obtain personal and sensitive information. The suggested Extreme Learning Machine (ELM)-based version has proven effective in identifying phishing websites. Internet page types exhibit diverse characteristics, requiring the utilization of a set of web page features for protection against phishing attacks. To counter these threats, a machine learning strategy is implemented. The phishing dataset, including authentic URLs from the database and collected data, undergoes pre-processing. Four groups of URL characteristics—domain-based, address-based, anomalous-based, and HTML or JavaScript features are employed for phishing detection. The analysed data is utilized to extract URL characteristics and generate corresponding attribute values. Machine learning approaches are applied to analyse URLs, establishing threshold and range values for URL properties. The project aims to develop an ELM categorization for various database characteristics and identify potential phishing sites. |
| Keywords: | Extreme Learning Machine (ELM), Support Vector Machine (SVM), Random Forest Algorithm, URL Phishing Websites, Browser add-ons. |
| Cite Article: | "Detection of Phishing Websites Using Machine Learning", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 3, page no.b829-b833, March-2024, Available :http://www.ijnrd.org/papers/IJNRD2403190.pdf |
| Downloads: | 00037 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 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:IJNRD2403190 Registration ID: 209355 Published In: Volume 9 Issue 3, March-2024 DOI (Digital Object Identifier): Page No: b829-b833 Country: Pune, Maharashtra, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2403190 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2403190 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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