Journal Articles (All Issues)

Abstract

Static and dynamic analysis are the two categories into which malware detection techniques can be divided. Each class's conventional methods have benefits and drawbacks of their own. For instance, although dynamic analysis is slower and needs more resources, it can detect malware variants created through code obfuscation more successfully than static analysis, which is faster but unable to do so. In this research, a novel ensemble model for malware detection is proposed that mitigate above discussed problem. Gradient Boosting (GB), Support Vector Machine (SVM), AdaBoost and Logistic regression (LR) are integrated to form an ensemble model. Initially a dataset known as CIC-Malmem 2022 is used for training and testing of the ensemble model. Term frequency-inverse document frequency (TF-IDF) technique is used to extract vectorized features in malware detection followed by preprocessing of the data. After this the least absolute shrinkage and selection operator(LASSO) tool is used to select the important features from the extracted features. Based on the selected features the ensemble model is trained and tested for performance evaluation. Finally, the result shows that as compared to individual classification of machine learning (ML) model. the classification performed by ensemble model is much accurate as the overall classification accuracy of the ensemble model is 99.99%. The proposed ensemble model is also contrasted with earlier developed hybrid model on the basis of accuracy and result shows that the suggested model outperformed the earlier developed model.

References

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