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Title: :  CYBERSECURITY VIGILANCE: LEVERAGING MACHINE LEARNING FOR SWIFT PHISHING WEBSITE DETECTION
PaperId: :  22981
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 10 Issue 2 2024
DUI:    16.0415/IJARIIE-22981
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
PRADAKSHINAA PBannari Amman Institute of Technology
JAYASURYA KBannari Amman Institute of Technology

Abstract

COMPUTER SCIENCE
Cyber Attacks,Machine Learning,Predictive Analysis,Data Science,Security,Fast API Deployment
In the present hyper-associated world, the raising danger of digital assaults represents a significant gamble to people, associations, and countries. Anticipating and moderating these dangers has become principal. This venture dives into the domain of digital protection by saddling the force of information science and AI procedures to foresee and forestall digital assaults.Key libraries like Pandas, NumPy, Seaborn, Matplotlib, Plotly, and Time are basic to our venture. They empower information control, representation, and transient investigation, giving basic bits of knowledge into digital assault designs.AI models are at the core of our prescient framework. We utilize Calculated Relapse and Multinomial Credulous Bayes calculations, engaged by instruments like train_test_split for information parting and classification_report and confusion_matrix for execution assessment. Moreover, RegexpTokenizer and SnowballStemmer upgrade text preprocessing, while CountVectorizer works with highlight extraction. The pipeline is smoothed out utilizing make_pipeline to improve model preparation.Understanding assault vectors and weaknesses is fundamental, which is where Picture,Word Cloud, Beautiful Soup, Selenium, and NetworkX become possibly the most important factor. Beautiful Soup and Selenium help in web scratching for constant danger information, while NetworkX helps with dissecting network structures.Moreover, to guarantee smooth sending and constant observing, we depend on uvicorn and fastapi for building hearty Programming interface, working with connection with the prescient model. At long last, joblib guarantees model ingenuity for consistent combination into creation frameworks.This project amalgamates these libraries and strategies, making a complete answer for foreseeing and forestalling digital assaults, at last bracing our computerized world's security foundation.

Citations

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IJARIIE PRADAKSHINAA P, and JAYASURYA K. "CYBERSECURITY VIGILANCE: LEVERAGING MACHINE LEARNING FOR SWIFT PHISHING WEBSITE DETECTION" International Journal Of Advance Research And Innovative Ideas In Education Volume 10 Issue 2 2024 Page 2207-2218
MLA PRADAKSHINAA P, and JAYASURYA K. "CYBERSECURITY VIGILANCE: LEVERAGING MACHINE LEARNING FOR SWIFT PHISHING WEBSITE DETECTION." International Journal Of Advance Research And Innovative Ideas In Education 10.2(2024) : 2207-2218.
APA PRADAKSHINAA P, & JAYASURYA K. (2024). CYBERSECURITY VIGILANCE: LEVERAGING MACHINE LEARNING FOR SWIFT PHISHING WEBSITE DETECTION. International Journal Of Advance Research And Innovative Ideas In Education, 10(2), 2207-2218.
Chicago PRADAKSHINAA P, and JAYASURYA K. "CYBERSECURITY VIGILANCE: LEVERAGING MACHINE LEARNING FOR SWIFT PHISHING WEBSITE DETECTION." International Journal Of Advance Research And Innovative Ideas In Education 10, no. 2 (2024) : 2207-2218.
Oxford PRADAKSHINAA P, and JAYASURYA K. 'CYBERSECURITY VIGILANCE: LEVERAGING MACHINE LEARNING FOR SWIFT PHISHING WEBSITE DETECTION', International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 2, 2024, p. 2207-2218. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/CYBERSECURITY_VIGILANCE__LEVERAGING_MACHINE_LEARNING_FOR_SWIFT_PHISHING_WEBSITE_DETECTION_ijariie22981.pdf (Accessed : ).
Harvard PRADAKSHINAA P, and JAYASURYA K. (2024) 'CYBERSECURITY VIGILANCE: LEVERAGING MACHINE LEARNING FOR SWIFT PHISHING WEBSITE DETECTION', International Journal Of Advance Research And Innovative Ideas In Education, 10(2), pp. 2207-2218IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/CYBERSECURITY_VIGILANCE__LEVERAGING_MACHINE_LEARNING_FOR_SWIFT_PHISHING_WEBSITE_DETECTION_ijariie22981.pdf (Accessed : )
IEEE PRADAKSHINAA P, and JAYASURYA K, "CYBERSECURITY VIGILANCE: LEVERAGING MACHINE LEARNING FOR SWIFT PHISHING WEBSITE DETECTION," International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 2, pp. 2207-2218, Mar-App 2024. [Online]. Available: https://ijariie.com/AdminUploadPdf/CYBERSECURITY_VIGILANCE__LEVERAGING_MACHINE_LEARNING_FOR_SWIFT_PHISHING_WEBSITE_DETECTION_ijariie22981.pdf [Accessed : ].
Turabian PRADAKSHINAA P, and JAYASURYA K. "CYBERSECURITY VIGILANCE: LEVERAGING MACHINE LEARNING FOR SWIFT PHISHING WEBSITE DETECTION." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 10 number 2 ().
Vancouver PRADAKSHINAA P, and JAYASURYA K. CYBERSECURITY VIGILANCE: LEVERAGING MACHINE LEARNING FOR SWIFT PHISHING WEBSITE DETECTION. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2024 [Cited : ]; 10(2) : 2207-2218. Available from: https://ijariie.com/AdminUploadPdf/CYBERSECURITY_VIGILANCE__LEVERAGING_MACHINE_LEARNING_FOR_SWIFT_PHISHING_WEBSITE_DETECTION_ijariie22981.pdf
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