  Cybersecurity is a major concern due to the increasing reliance on technology
and interconnected systems. Malware detectors help mitigate cyber-attacks by
comparing malware signatures. Machine learning can improve these detectors by
automating feature extraction, identifying patterns, and enhancing dynamic
analysis. In this paper, the performance of six multiclass classification
models is compared on the Malimg dataset, Blended dataset, and Malevis dataset
to gain insights into the effect of class imbalance on model performance and
convergence. It is observed that the more the class imbalance less the number
of epochs required for convergence and a high variance across the performance
of different models. Moreover, it is also observed that for malware detectors
ResNet50, EfficientNetB0, and DenseNet169 can handle imbalanced and balanced
data well. A maximum precision of 97% is obtained for the imbalanced dataset, a
maximum precision of 95% is obtained on the intermediate imbalance dataset, and
a maximum precision of 95% is obtained for the perfectly balanced dataset.
