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Designing Deep Convolutional Neural Networks using a Genetic Algorithm for Image-based Malware Classification

18 Jul 2022IEEE Congress on Evolutionary Computation (CEC) 2022 7archive 2025-07-28

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

Evolutionary AlgorithmsMalware Classification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
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

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