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Executable bytes can be transformed into image tensors and classified by convolutional neural networks, but the strongest directly matching 2026 evidence is not an RGB benchmark. Saxena and Das demonstrated hierarchical detection and attribution with 224×224 grayscale images of Windows Portable Executable (PE) files: one model predicted benign versus malware, one of five families, and one of 33 subfamilies in a single forward pass. Subfamily accuracy reached 93–94% on their curated test set. A separate RGB study adds binary, assembly, and API-derived channels and uses hybrid multi-head attention, but it should be treated as related work rather than proof that RGB attention achieves those same hierarchical results.
What multi-level malware image attribution means
Traditional malware analysis can depend on execution traces, disassembly, API calls, or manually engineered byte features. Image-based classification takes another route: it lays out the contents of an executable as pixels, then lets a vision model learn recurring visual patterns.
The closest directly matching hierarchical study uses Windows PE files converted into normalized 224×224 grayscale images. The image is a representation of the file’s bytes, not a screenshot of its behavior. A convolutional network extracts spatial patterns, and a multi-output model produces three related decisions:
- Whether the file is benign or malicious.
- Which of five malware families it belongs to.
- Which of 33 malware subfamilies is the most likely match.
Because the outputs are generated together, the design can preserve the relationship between a broad detection decision and a more specific attribution decision. A file may be identified as malware even when the model cannot reliably separate two closely related subfamilies.
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What the 2026 hierarchical benchmark reported
Saxena and Das trained three versions of the classifier, changing the sequence-modeling or capsule head attached to the convolutional feature extractor. Their reported test accuracies were:
| Model head | Binary accuracy | Family accuracy | Subfamily accuracy |
|---|---|---|---|
| CNN + Temporal Convolutional Network (TCN) | 99% | 98% | 94% |
| CNN + Capsule Network (CapsNet) | 100% | 97% | 93% |
| CNN + Bidirectional LSTM (BiLSTM) | 100% | 98% | 94% |
The pattern is important: performance declines as the label becomes more specific. Broad family characteristics can remain visible across many samples, while subfamily attribution must distinguish variants that may share loaders, encryption routines, persistence code, or large portions of their underlying implementation.
These percentages describe the study’s curated benchmark. They are evidence that the task is feasible under those data and split conditions, not a guarantee of the same accuracy on newly emerging malware, samples from another region, or files deliberately engineered to evade image-based classifiers.
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How the executable-image pipeline works
1. Convert PE bytes into a fixed image
Each Windows PE file is represented as a normalized 224×224 grayscale image. Mapping bytes to intensity values gives the model a fixed-size tensor even though executable files have different lengths. The resulting texture can reflect headers, code regions, embedded data, padding, and other layout regularities.
2. Learn visual features with a CNN
Convolutional layers scan local neighborhoods and build increasingly abstract features. The network does not need a human to define a signature for a particular family; it learns patterns associated with the labels supplied during training.
3. Produce all three labels together
The hierarchical model has outputs for detection, family identification, and subfamily attribution. This is why the authors describe a single forward pass: one evaluation of the file yields predictions at every level instead of requiring separate detectors for each decision.
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Where RGB and attention fit
The RGB portion of the topic comes from related work, not from the grayscale hierarchical benchmark. The RGB approach creates three information channels intended to combine binary content with assembly details and API information. Instead of treating the channels as interchangeable color, the representation uses them to carry different views of the same malicious code.
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Attention weights are not automatically causal explanations. A bright or highly weighted area in an attention map does not, by itself, prove that the corresponding bytes caused the malware decision. Security interpretation requires checking whether highlighted regions align with meaningful PE structures, assembly instructions, or API evidence and whether the pattern remains stable under controlled changes.
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| Aspect | Hierarchical benchmark | Related RGB-attention work |
|---|---|---|
| Primary representation | Normalized 224×224 grayscale image of PE bytes | RGB channels combining binary, assembly, and API-derived information |
| Prediction emphasis | Benign/malware, five families, and 33 subfamilies | Visual malicious-code classification with enriched features |
| Model mechanism | TCN, CapsNet, or BiLSTM heads attached to a CNN | Hybrid multi-head channel and spatial attention |
| What can be concluded | Subfamily classification reached 93–94% on its stated benchmark | RGB and attention provide a plausible richer representation; equivalent hierarchical accuracy is not established by the grayscale results |
Data and training details that affect the numbers
The hierarchical study combined publicly available malware repositories with curated benign executables. For each malware subfamily, it describes 300 training images, 50 validation images, and 50 test images. Benign files were divided using the same proportions. This fixed per-subfamily design makes comparisons easier than a naturally occurring malware population, where some families dominate and many others are rare.
- Framework: TensorFlow and Keras.
- Optimizer and learning rate: Adam at 0.0001.
- Batch size: 32.
- Training limit: up to 50 epochs.
- Regularization and stopping: dropout, early stopping, and checkpointing.
- Reported hardware: an NVIDIA A100 GPU.
The published description does not turn these settings into a universal recipe. Results can change with duplicate files, family definitions, packing, class prevalence, and whether samples are separated randomly or by collection date. A random split can also look more favorable when near-duplicate variants appear in both training and test data.
Why unseen malware remains difficult
Variant similarity
Subfamilies often share code and infrastructure. Small changes to configuration, encryption, or a loader can produce a new variant whose image remains close to samples seen during training, while a genuinely new family may have no learned visual analogue.
Packing and obfuscation
Packers can replace visible code patterns with compressed or encrypted regions. That may cause the model to classify the packer or file layout rather than the underlying behavior.
File-format and collection drift
Changes in compiler, build process, PE layout, or the source of collected samples can alter image statistics without changing the operational threat. A model validated on one repository may therefore degrade on another.
Evaluation design
A useful deployment assessment should report more than one accuracy number. It should specify class balance, provenance, duplicate handling, temporal separation, confidence calibration, and performance on packed or obfuscated files. Family and subfamily confusion matrices are especially valuable because an apparently high overall score can hide failure on a small but important class.
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How to reproduce the approach responsibly
- Define the label hierarchy. Decide what counts as benign, which families are included, and how subfamilies are assigned. Keep those definitions fixed before training.
- Collect and deduplicate PE files. Record source, collection date, hashes, architecture, and packing status. Prevent near-duplicates from crossing the evaluation boundary.
- Generate the representation. Convert bytes to normalized 224×224 grayscale tensors for a direct reproduction, or build separate RGB channels only when assembly and API extraction are consistently available.
- Split by realistic deployment conditions. In addition to a conventional test set, use a time-based or source-held-out set to measure drift and provenance effects.
- Train with reproducible controls. Use the stated TensorFlow/Keras settings as a starting point, retain checkpoints, and document GPU, random seeds, preprocessing, and early-stopping behavior.
- Report every level separately. Publish binary, family, and subfamily metrics, per-class results, confusion matrices, and abstention or confidence behavior.
- Validate explanations. Compare attention or saliency regions with PE sections, disassembly, and API evidence before presenting them to analysts as useful clues.
What security teams should take from the evidence
Image-based deep learning is a practical way to add a fast static signal to a malware-analysis pipeline. The 2026 benchmark shows that one hierarchical model can separate benign files, identify broad families, and attempt subfamily attribution without relying on handcrafted runtime features. The RGB-attention line of work suggests that multiple code views may provide richer signals than a single grayscale byte image.
Neither result replaces sandboxing, reverse engineering, signature systems, or analyst judgment. The central operational question is not whether a model can post a high score on a curated test set; it is whether its errors are understood, its confidence is calibrated, and its performance remains acceptable when the next campaign uses a different packer, compiler, repository, or family.
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