Proactive Detection of Malicious Webpages Using Hybrid Natural Language Processing and Ensemble Learning Techniques
The proliferation of malicious webpages presents a growing threat to online security, necessitating advanced detection methods to mitigate risks. This paper proposes a novel approach that integrates Natural Language Processing (NLP) techniques with an ensemble of machine learning models for the proa...
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| Format: | Article |
| Language: | English |
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University of Zagreb, Faculty of organization and informatics
2024-01-01
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| Series: | Journal of Information and Organizational Sciences |
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| Online Access: | https://hrcak.srce.hr/file/471912 |
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| author | Althaf Ali A Rama Devi K Syed Siraj Ahmed N Ramchandran P Parvathi S |
| author_facet | Althaf Ali A Rama Devi K Syed Siraj Ahmed N Ramchandran P Parvathi S |
| author_sort | Althaf Ali A |
| collection | DOAJ |
| description | The proliferation of malicious webpages presents a growing threat to online security, necessitating advanced detection methods to mitigate risks. This paper proposes a novel approach that integrates Natural Language Processing (NLP) techniques with an ensemble of machine learning models for the proactive detection of malicious web content. By leveraging semantic analysis, lexical patterns, and metadata extraction, the proposed framework enhances the identification of suspicious patterns in web page content. The ensemble model combines decision trees, random forests, and gradient boosting methods, optimizing classification accuracy and reducing false positives. A comprehensive evaluation using a large dataset of web pages, including both benign and malicious examples, demonstrates the superiority of the proposed method over traditional single-model approaches. With accuracy rates exceeding 98%, this framework achieves a robust, scalable solution for real-time web content analysis, providing a critical tool for cybersecurity professionals to detect and block malicious webpages before they can cause harm. Future directions include the integration of deep learning architectures and adaptive filtering techniques to further refine detection capabilities. |
| format | Article |
| id | doaj-art-f7868609338142deb1f2d3c54b404751 |
| institution | Kabale University |
| issn | 1846-3312 1846-9418 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | University of Zagreb, Faculty of organization and informatics |
| record_format | Article |
| series | Journal of Information and Organizational Sciences |
| spelling | doaj-art-f7868609338142deb1f2d3c54b4047512025-01-10T10:06:03ZengUniversity of Zagreb, Faculty of organization and informaticsJournal of Information and Organizational Sciences1846-33121846-94182024-01-0148229530910.31341/jios.48.2.4Proactive Detection of Malicious Webpages Using Hybrid Natural Language Processing and Ensemble Learning TechniquesAlthaf Ali A0Rama Devi K1Syed Siraj Ahmed N2Ramchandran P3Parvathi S4Department of Computer Application, Madanapalle Institute of Technology & Science, Madanapalle, IndiaDepartment of Information Technology, Panimalar Engineering College, Chennai, IndiaSchool of Computer Science Engineering and Information Science, Presidency University, Bangalore, IndiaDepartment of computer Application, Parul institute of engineering and technology, Parul University, P.O.limda, Tal.waghodia, Dist.Vadodra, IndiaDepartment of Computer Science and Engineering, Erode Sengunthar Engineering College, Erode, IndiaThe proliferation of malicious webpages presents a growing threat to online security, necessitating advanced detection methods to mitigate risks. This paper proposes a novel approach that integrates Natural Language Processing (NLP) techniques with an ensemble of machine learning models for the proactive detection of malicious web content. By leveraging semantic analysis, lexical patterns, and metadata extraction, the proposed framework enhances the identification of suspicious patterns in web page content. The ensemble model combines decision trees, random forests, and gradient boosting methods, optimizing classification accuracy and reducing false positives. A comprehensive evaluation using a large dataset of web pages, including both benign and malicious examples, demonstrates the superiority of the proposed method over traditional single-model approaches. With accuracy rates exceeding 98%, this framework achieves a robust, scalable solution for real-time web content analysis, providing a critical tool for cybersecurity professionals to detect and block malicious webpages before they can cause harm. Future directions include the integration of deep learning architectures and adaptive filtering techniques to further refine detection capabilities.https://hrcak.srce.hr/file/471912CountTerm frequency and Inverse document frequencyMachine learning modelPhishingMalicious webpages |
| spellingShingle | Althaf Ali A Rama Devi K Syed Siraj Ahmed N Ramchandran P Parvathi S Proactive Detection of Malicious Webpages Using Hybrid Natural Language Processing and Ensemble Learning Techniques Journal of Information and Organizational Sciences Count Term frequency and Inverse document frequency Machine learning model Phishing Malicious webpages |
| title | Proactive Detection of Malicious Webpages Using Hybrid Natural Language Processing and Ensemble Learning Techniques |
| title_full | Proactive Detection of Malicious Webpages Using Hybrid Natural Language Processing and Ensemble Learning Techniques |
| title_fullStr | Proactive Detection of Malicious Webpages Using Hybrid Natural Language Processing and Ensemble Learning Techniques |
| title_full_unstemmed | Proactive Detection of Malicious Webpages Using Hybrid Natural Language Processing and Ensemble Learning Techniques |
| title_short | Proactive Detection of Malicious Webpages Using Hybrid Natural Language Processing and Ensemble Learning Techniques |
| title_sort | proactive detection of malicious webpages using hybrid natural language processing and ensemble learning techniques |
| topic | Count Term frequency and Inverse document frequency Machine learning model Phishing Malicious webpages |
| url | https://hrcak.srce.hr/file/471912 |
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