# Comparative Analysis of Imbalanced Malware Byteplot Image Classification using Transfer Learning Source: [Comparative Analysis of Imbalanced Malware Byteplot Image Classification using Transfer Learning]() Collection index: [llms.txt]() Data updated: 2025-10-26T02:10:33.419Z Authors: Jayasudha M, Ayesha Shaik, Gaurav Pendharkar, Soham Kumar, Muhesh Kumar B, Sudharshanan Balaji Published: 2023-10-04T11:33:36.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()