Vol. 4 No. 3 (2022), Articles
Vol. 4 No. 3 (2022)

Inhibiting Webshell Attacks by Random Forest Ensembles with XGBoost

Articles
Published 20-08-2022
D. Sasikala
Professor, Department of Computer Science & Engineering, JB Institute of Engineering & Technology, Moinabad, Hyderabad, India
D. Chandrakanth
UG Scholars, Department of Computer Science & Engineering, JB Institute of Engineering & Technology, Moinabad, Hyderabad, India
C. Sai Pranathi Reddy
UG Scholars, Department of Computer Science & Engineering, JB Institute of Engineering & Technology, Moinabad, Hyderabad, India
J. Jitendra Teja
UG Scholars, Department of Computer Science & Engineering, JB Institute of Engineering & Technology, Moinabad, Hyderabad, India
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Keywords

Webshell attacks
benign
spam and malicious websites
uniform resource locator
browser-based vulnerabilities

How to Cite

Inhibiting Webshell Attacks by Random Forest Ensembles with XGBoost. (2022). Journal of Information Technology and Digital World, 4(3), 153-166. https://doi.org/10.36548/jitdw.2022.3.003
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Abstract

Malign websites effectively endorse the evolution of web illicit events and force the progression of Web services. As an efficient outcome, there is powerful enthusiasm to create systemic resolutions in inhibiting the client from the call onto such Websites. Knowledge-centered Random Forest outfits with XGBoost tactic is recommended for categorizing Websites into 3 categories: Benign, Spam and Malicious. This practice evaluates the Uniform Resource Locator in the situation deprived of accessing the matter of Websites. Thus, it wipes out the run-time expectation and the likelihood of uncovering clients to the browser aimed susceptibilities. As a consequence of involving Random Forest Ensembles with XGBoost, it realizes superior enactment on expansive view and publicity correlated with blacklisting amenity. Preprocessing is performed in order to improve the quality of the data subsequently, analyze certain algorithms, thereby explore the best model are the facts discussed in this research. Work also continues to probe how well this chosen archetypal will operate in the future ahead.

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References

G. Aaron and R. Rasmussen, “Global phishing survey: Trends and domain name use in 2016,” 2016.

Gupta, Brij B., Aakanksha Tewari, Ankit Kumar Jain, and Dharma P. Agrawal. "Fighting against phishing attacks: state of the art and future challenges." Neural Computing and Applications 28, no. 12 (2017): 3629-3654.

Aleroud, Ahmed, and Lina Zhou. "Phishing environments, techniques, and countermeasures: A survey." Computers & Security 68 (2017): 160-196.

Aaron, G., and R. Rasmussen. "Phishing Activity Trends Report, 4th Quarter 2015." (2016).

Verma, Rakesh, Narasimha Shashidhar, and Nabil Hossain. "Detecting phishing emails the natural language way." In European Symposium on Research in Computer Security, pp. 824-841. Springer, Berlin, Heidelberg, 2012.

Khonji, Mahmoud, Youssef Iraqi, and Andrew Jones. "Phishing detection: a literature survey." IEEE Communications Surveys & Tutorials 15, no. 4 (2013): 2091-2121.

Park, Gilchan, and Julia M. Taylor. "Using syntactic features for phishing detection." arXiv preprint arXiv:1506.00037 (2015).

Dazeley, Richard, John L. Yearwood, Byeong H. Kang, and Andrei V. Kelarev. "Consensus clustering and supervised classification for profiling phishing emails in internet commerce security." In Pacific Rim Knowledge Acquisition Workshop, pp. 235-246. Springer, Berlin, Heidelberg, 2010.

Whittaker, Colin, Brian Ryner, and Marria Nazif. "Large-scale automatic classification of phishing pages." (2010).

Seiffert, Chris, Taghi M. Khoshgoftaar, Jason Van Hulse, and Amri Napolitano. "RUSBoost: Improving classification performance when training data is skewed." In 2008 19th international conference on pattern recognition, pp. 1-4. IEEE, 2008.

Sabhnani, Maheshkumar, and Gürsel Serpen. "Application of machine learning algorithms to KDD intrusion detection dataset within misuse detection context." In MLMTA, pp. 209-215. 2003.

Dredze, Mark, Reuven Gevaryahu, and Ari Elias-Bachrach. "Learning fast classifiers for image spam." In CEAS, pp. 2007-487. 2007.

Park, Gilchan, and Julia M. Taylor. "Using syntactic features for phishing detection." arXiv preprint arXiv:1506.00037 (2015).

Verma, Rakesh, Narasimha Shashidhar, and Nabil Hossain. "Detecting phishing emails the natural language way." In European Symposium on Research in Computer Security, pp. 824-841. Springer, Berlin, Heidelberg, 2012.

Ronda, Troy, Stefan Saroiu, and Alec Wolman. "Itrustpage: a user-assisted anti-phishing tool." ACM SIGOPS Operating Systems Review 42, no. 4 (2008): 261-272.

Ahmed Aleroud, and Lina Zhou, “Phishing Environments, Techniques, and Countermeasures: A Survey”, Computers & Security, Vol. 68, No.9, pp. 1-44, April 2017.

Khonji, Mahmoud, Youssef Iraqi, and Andrew Jones. "Phishing detection: a literature survey." IEEE Communications Surveys & Tutorials 15, no. 4 (2013): 2091-2121.

L'Huillier, Gaston, Richard Weber, and Nicolas Figueroa. "Online phishing classification using adversarial data mining and signaling games." In Proceedings of the ACM SIGKDD Workshop on CyberSecurity and Intelligence Informatics, pp. 33-42. 2009.