NoPhish: Efficient Chrome Extension for Phishing Detection Using Machine Learning Techniques
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912031012028416 |
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| author | Thaqi, Leand Halili, Arbnor Vishi, Kamer Rexha, Blerim |
| author_facet | Thaqi, Leand Halili, Arbnor Vishi, Kamer Rexha, Blerim |
| contents | The growth of digitalization services via web browsers has simplified our daily routine of doing business. But at the same time, it has made the web browser very attractive for several cyber-attacks. Web phishing is a well-known cyberattack that is used by attackers camouflaging as trustworthy web servers to obtain sensitive user information such as credit card numbers, bank information, personal ID, social security number, and username and passwords. In recent years many techniques have been developed to identify the authentic web pages that users visit and warn them when the webpage is phishing. In this paper, we have developed an extension for Chrome the most favorite web browser, that will serve as a middleware between the user and phishing websites. The Chrome extension named "NoPhish" shall identify a phishing webpage based on several Machine Learning techniques. We have used the training dataset from "PhishTank" and extracted the 22 most popular features as rated by the Alexa database. The training algorithms used are Random Forest, Support Vector Machine, and k-Nearest Neighbor. The performance results show that Random Forest delivers the best precision. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_10547 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | NoPhish: Efficient Chrome Extension for Phishing Detection Using Machine Learning Techniques Thaqi, Leand Halili, Arbnor Vishi, Kamer Rexha, Blerim Cryptography and Security Artificial Intelligence Machine Learning The growth of digitalization services via web browsers has simplified our daily routine of doing business. But at the same time, it has made the web browser very attractive for several cyber-attacks. Web phishing is a well-known cyberattack that is used by attackers camouflaging as trustworthy web servers to obtain sensitive user information such as credit card numbers, bank information, personal ID, social security number, and username and passwords. In recent years many techniques have been developed to identify the authentic web pages that users visit and warn them when the webpage is phishing. In this paper, we have developed an extension for Chrome the most favorite web browser, that will serve as a middleware between the user and phishing websites. The Chrome extension named "NoPhish" shall identify a phishing webpage based on several Machine Learning techniques. We have used the training dataset from "PhishTank" and extracted the 22 most popular features as rated by the Alexa database. The training algorithms used are Random Forest, Support Vector Machine, and k-Nearest Neighbor. The performance results show that Random Forest delivers the best precision. |
| title | NoPhish: Efficient Chrome Extension for Phishing Detection Using Machine Learning Techniques |
| topic | Cryptography and Security Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2409.10547 |