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Designing Deep Convolutional Neural Networks using a Genetic Algorithm for Image-based Malware Classification
Cornelius Paardekooper, Nasimul Noman, Raymond Chiong, Vijay Varadharajan
In recent years, deep Convolutional Neural Networks (CNNs) have shown great potential in malware classification. CNNs, which are originally designed for image processing, identify malware binaries visualised as images. Despite offering promising performance, these human-designed networks are very large requiring more resources to train and deploy them. Evolutionary algorithms have been successfully used in designing deep neural networks automatically for different application domains. In this work, we use a Genetic Algorithm (GA) to optimise the CNN topology and hyperparameters for image-based malware classification. Computational experiments with two different malware datasets, Malimg and Microsoft Malware, show that the GA-evolved networks are very competitive to the networks designed by experts in classifying malware, yet they are also considerably smaller in size comparison.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Malware Classification | Malimg Dataset | GA Designed Deep CNN | Accuracy | 0.985 | #2 of 5 | Archive leaderboard | report |
| Malware Classification | Malimg Dataset | GA Designed Deep CNN | Macro F1 | 0.9391 | #2 of 5 | Archive leaderboard | report |
| Malware Classification | Microsoft Malware Classification Challenge | GA Designed Deep CNN | Accuracy | 0.9307 | #29 of 29 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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