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A Hierarchical Convolutional Neural Network for Malware Classification

30 Sep 2019archive 2025-07-28

Daniel Gibert, Carles Mateu, Jordi Planes

Malware detection and classification is a challenging problem and an active area of research. Particular challenges include how to best treat and preprocess malicious executables in order to feed machine learning algorithms. Novel approaches in the literature treat an executable as a sequence of bytes or as a sequence of assembly language instructions. However, in those approaches the hierarchical structure of programs is not taken into consideration. An executable exhibits various levels of spatial correlation. Adjacent code instructions are correlated spatially but that is not necessarily the case. Function calls and jump commands transfer the control of the program to a different point in the instruction stream. Furthermore, these discontinuities are maintained when treating the binary as a sequence of byte values. In addition, functions might be arranged randomly if addresses are correctly reorganized. To address these issues we propose a Hierarchical Convolutional Network (HCN) for malware classification. It has two levels of convolutional blocks applied at the mnemonic-level and at the function-level, enabling us to extract n-gram like features from both levels when constructing the malware representation. We validate our HCN method on the dataset released for the Microsoft Malware Classification Challenge, outperforming almost every deep learning method in the literature.

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Tasks

ClassificationGeneral ClassificationMalware ClassificationMalware Detection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Malware Classification Microsoft Malware Classification Challenge Hierarchical Convolutional Network Accuracy (10-fold) 0.9913 #6 of 29 Archive leaderboard report
Malware Classification Microsoft Malware Classification Challenge Hierarchical Convolutional Network LogLoss 0.0419 #6 of 29 Archive leaderboard report
Malware Classification Microsoft Malware Classification Challenge Hierarchical Convolutional Network Macro F1 (10-fold) 0.9830 #6 of 29 Archive leaderboard report
Malware Classification Microsoft Malware Classification Challenge CNN+BiLSTM Accuracy (10-fold) 0.9820 #12 of 29 Archive leaderboard report
Malware Classification Microsoft Malware Classification Challenge CNN+BiLSTM LogLoss 0.0744 #12 of 29 Archive leaderboard report
Malware Classification Microsoft Malware Classification Challenge CNN+BiLSTM Macro F1 (10-fold) 0.9605 #12 of 29 Archive leaderboard report
Malware Classification Microsoft Malware Classification Challenge DeepConv Accuracy (10-fold) 0.9756 #15 of 29 Archive leaderboard report
Malware Classification Microsoft Malware Classification Challenge DeepConv LogLoss 0.1602 #15 of 29 Archive leaderboard report
Malware Classification Microsoft Malware Classification Challenge DeepConv Macro F1 (10-fold) 0.9071 #15 of 29 Archive leaderboard report
Malware Classification Microsoft Malware Classification Challenge Hierarchical Attention Network Accuracy (10-fold) 0.9742 #17 of 29 Archive leaderboard report
Malware Classification Microsoft Malware Classification Challenge Hierarchical Attention Network LogLoss 0.0933 #17 of 29 Archive leaderboard report
Malware Classification Microsoft Malware Classification Challenge Hierarchical Attention Network Macro F1 (10-fold) 0.9468 #17 of 29 Archive leaderboard report
Malware Classification Microsoft Malware Classification Challenge MalConv Accuracy (10-fold) 0,9641 #24 of 29 Archive leaderboard report
Malware Classification Microsoft Malware Classification Challenge MalConv LogLoss 0.3071 #24 of 29 Archive leaderboard report
Malware Classification Microsoft Malware Classification Challenge MalConv Macro F1 (10-fold) 0.8902 #24 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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