Abstract: Malware detection is a critical aspect of cybersecurity, and the integration of machine learning techniques has shown promising results in enhancing detection capabilities. This paper proposes the integration of the Extreme Gradient Boosting (XGBoost) algorithm for detecting malware. XGBoost, known for its scalability and efficiency, has been successfully applied in various domains, including malware detection. By leveraging XGBoost, this research aims to improve the accuracy and efficiency of malware detection systems. Additionally, the XGBoost algorithm has been compared with other machine learning models like Random Forest and Adaboost, consistently showing superior performance in terms of detection accuracy. The proposed approach in this paper builds on the strengths of XGBoost to enhance the detection of diverse malware variants. By optimizing XGBoost parameters and leveraging ensemble learning techniques, this research contributes to advancing the capabilities of malware detection systems, thereby strengthening cybersecurity measures in the face of evolving cyber threats.
Keywords: Malware Detection, Xgboost, Machine Learning, Dynamic Analysis, DT,KNN,RF, Static analysis.
Cite:
Naveen Sundar Kumar P, Veera Prasad Singiri, Sujatha Perapogu, Yasaswini Kunam, Vamse Krishna Mallela, "Enhanced Malware Detection Using Machine Learning Algorithms", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 4, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13422.
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DOI:
10.17148/IJARCCE.2024.13422
