Journal Articles (All Issues)

Abstract

Malicious software, or malware, is a growing problem in today's cyber landscape and threatens the availability, confidentiality, and integrity of digital information. An effective malware detection system can be designed with the help of ensemble machine-learning models, according to this research paper. This research makes use of the Canadian Institute for Cybersecurity's CIC-Malmem2022 dataset, which was designed for studies on complicated malware classification. To strengthen the malware detection model's accuracy and resilience, the suggested approach combines Principal Component Analysis, Recursive Feature Eliminator, Decision Trees, Light Gradient Boosting Machine, and Gradient Boosting. Although the ensemble model achieved a high level of accuracy (99.96%) on the test set, the results demonstrate its effectiveness. Model hyperparameter tuning reveals best-practice parameters, and the ensemble confusion matrix delves into classification efficacy. Analyses comparing the proposed approach to current methods show that it is superior at detecting malware. The study finishes with suggestions for a safe environment to deploy the model and for frequent updates to address shifting cybersecurity threats.

References

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