An Effective Detection Approach for Phishing URL Using ResMLP
Phishing websites, mimicking legitimate counterparts, pose significant threats by stealing user information through deceptive Uniform Resource Locators (URLs). Traditional blacklists struggle to identify dynamic URLs, necessitating advanced detection mechanisms. In this study, we propose an effectiv...
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10546980/ |
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| author | S. Remya Manu J. Pillai Kajal K. Nair Somula Rama Subbareddy Yong Yun Cho |
| author_facet | S. Remya Manu J. Pillai Kajal K. Nair Somula Rama Subbareddy Yong Yun Cho |
| author_sort | S. Remya |
| collection | DOAJ |
| description | Phishing websites, mimicking legitimate counterparts, pose significant threats by stealing user information through deceptive Uniform Resource Locators (URLs). Traditional blacklists struggle to identify dynamic URLs, necessitating advanced detection mechanisms. In this study, we propose an effective approach utilizing residual pipelining for phishing URL detection. Our method extracts common URL features and sentiments, employing a residual pipeline comprising convolutional and inverted residual blocks. These resultant features are then fed into a Multi-Layer Perceptron (MLP) for classification. We evaluate the efficacy of our approach against traditional algorithms using a Kaggle dataset. Our results demonstrate superior accuracy, precision, F1 Score, and recall, showcasing its effectiveness in mitigating phishing threats. Utilizing a residual pipeline made up of convolutional and inverted residual blocks, we start our method by identifying similar URL features and sentiments. We also use domain age research to figure out how long URLs have been around. Additionally, the lexical study of URL structure makes our method more useful, resulting in impressive accuracy. With an accuracy of 98.29%, this research highlights the importance of innovative techniques in combating evolving cyber threats. Future research directions could focus on enhancing the model’s robustness against adversarial attacks and integrating real-time monitoring for proactive defense strategies. |
| format | Article |
| id | doaj-art-25d4d67bd8ab475d8a7f5752dbfc5e43 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-25d4d67bd8ab475d8a7f5752dbfc5e432025-08-20T02:07:16ZengIEEEIEEE Access2169-35362024-01-0112793677938210.1109/ACCESS.2024.340904910546980An Effective Detection Approach for Phishing URL Using ResMLPS. Remya0https://orcid.org/0000-0003-2391-2013Manu J. Pillai1https://orcid.org/0000-0003-2938-0281Kajal K. Nair2https://orcid.org/0009-0004-6721-2048Somula Rama Subbareddy3Yong Yun Cho4https://orcid.org/0000-0002-4855-4163Amrita School of Computing, Amrita Vishwa Vidyapeetham, Amritapuri, Kollam, Kerala, IndiaDepartment of Computer Science and Engineering, TKM College of Engineering, Kollam, Kerala, IndiaDepartment of Computer Science and Engineering, TKM College of Engineering, Kollam, Kerala, IndiaDepartment of Information Technology, Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, IndiaDepartment of Information and Communication Engineering, Sunchon National University, Jeollanam-do, Suncheon, South KoreaPhishing websites, mimicking legitimate counterparts, pose significant threats by stealing user information through deceptive Uniform Resource Locators (URLs). Traditional blacklists struggle to identify dynamic URLs, necessitating advanced detection mechanisms. In this study, we propose an effective approach utilizing residual pipelining for phishing URL detection. Our method extracts common URL features and sentiments, employing a residual pipeline comprising convolutional and inverted residual blocks. These resultant features are then fed into a Multi-Layer Perceptron (MLP) for classification. We evaluate the efficacy of our approach against traditional algorithms using a Kaggle dataset. Our results demonstrate superior accuracy, precision, F1 Score, and recall, showcasing its effectiveness in mitigating phishing threats. Utilizing a residual pipeline made up of convolutional and inverted residual blocks, we start our method by identifying similar URL features and sentiments. We also use domain age research to figure out how long URLs have been around. Additionally, the lexical study of URL structure makes our method more useful, resulting in impressive accuracy. With an accuracy of 98.29%, this research highlights the importance of innovative techniques in combating evolving cyber threats. Future research directions could focus on enhancing the model’s robustness against adversarial attacks and integrating real-time monitoring for proactive defense strategies.https://ieeexplore.ieee.org/document/10546980/PhishingURL detectionresidual pipeliningcybersecurityclassification |
| spellingShingle | S. Remya Manu J. Pillai Kajal K. Nair Somula Rama Subbareddy Yong Yun Cho An Effective Detection Approach for Phishing URL Using ResMLP IEEE Access Phishing URL detection residual pipelining cybersecurity classification |
| title | An Effective Detection Approach for Phishing URL Using ResMLP |
| title_full | An Effective Detection Approach for Phishing URL Using ResMLP |
| title_fullStr | An Effective Detection Approach for Phishing URL Using ResMLP |
| title_full_unstemmed | An Effective Detection Approach for Phishing URL Using ResMLP |
| title_short | An Effective Detection Approach for Phishing URL Using ResMLP |
| title_sort | effective detection approach for phishing url using resmlp |
| topic | Phishing URL detection residual pipelining cybersecurity classification |
| url | https://ieeexplore.ieee.org/document/10546980/ |
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