Imbalanced malware detection via group-ensemble

HaiSheng Yan

doi:10.1117/12.2681076

23 May 2023 Imbalanced malware detection via group-ensemble

HaiSheng Yan

Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 126450N (2023) https://doi.org/10.1117/12.2681076
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China

Abstract

In the application of machine learning to malware detection, the number of normal software samples is usually much more than that of the malicious samples, which leads to imbalance problems in training data and arises great challenges to build classification models. To improve the classification accuracy, a group-ensemble method is proposed, where normal samples are divided into several groups using sampling technology. Then each group is combined with all malicious samples to form a training data set on which a classification model is learned. Finally, all classification models are integrated into one single classification using ensemble method. The experimental results over real-world data sets indicate that the proposed method can handle the imbalance problem in malware detection with improved detection accuracy.

Citation Download Citation

HaiSheng Yan "Imbalanced malware detection via group-ensemble", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 126450N (23 May 2023); https://doi.org/10.1117/12.2681076

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