Modified Weight Optimized XG Boost (MWO-XGB) for Concept Drift and Data Imbalance Problems in the Online Environment

Modified Weight Optimized XG Boost (MWO-XGB) for Concept Drift and Data Imbalance Problems in the Online Environment

© 2022 by IJETT Journal
Volume-70 Issue-6
Year of Publication : 2022
Authors : Sagargouda S Patil, Dinesha H.A.
DOI : 10.14445/22315381/IJETT-V70I6P232

How to Cite?

Sagargouda S Patil, Dinesha H.A., "Modified Weight Optimized XG Boost (MWO-XGB) for Concept Drift and Data Imbalance Problems in the Online Environment," International Journal of Engineering Trends and Technology, vol. 70, no. 6, pp. 308-316, 2022. Crossref,

Nowadays, many websites on the internet are being used for sharing information, connecting people, video streaming, browsing, etc. All these websites are accessed using the links which the host provides. The host provides the links with proper security and good content. But some of the sites have Malicious Uniform Resource Allocators (URL) using which the attacker can access the user information. When the user clicks or taps on the links or hyperlinks of these websites, then he is redirected to another website. In this case, the user has no idea that he is getting attacked by the user, and they are providing personal information to the attacker. Hence, in this paper, the machine learning system, XGBoost, using which the model can identify the malicious links, classify them and remove them using the proposed modified XGBoost model. In this paper, the proposed modified XGBoost method, Modified Weight Optimized XGBoost (MWO-XGB), detects the URL in an online environment with class imbalance and concept drift problems. This paper mainly focused on the popular NSL-KDD dataset and other social media datasets to identify and detect the malicious URL using the proposed model. The experimental results are better when compared with the existing system such as XGBoost etc. This model's main focus is to reduce the malicious attacks in the online environment using the MWO-XGB model.

Malicious URL, MWO-XGBoost, NSL-KDD, Attack.

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