What is Malware Visualization? How and Why create virus images

When everything went to hell and the CPU began spewing out random bits, the result, on a CLI machine, was lines and lines of perfectly formed but random characters on the screen known to cognoscenti as “going Cyrillic.” But to the MacOS, the screen was not a teletype, but a place to put graphics; the image on the screen was a bitmap, a literal rendering of the contents of a particular portion of the computer’s memory. When the computer crashed and wrote gibberish into the bitmap, the result was something that looked vaguely like static on a broken television set - a “snow crash.”

The intuition

Back in an era when deep learning and neural networks weren’t as ubiquitous as they are today, a team of researchers at UC Santa Barbara had a peculiar idea: _What if we could turn a malware binary file into an image?

Doing… what?

Whenever I explain these techniques to people, their first reaction is usually some variation of:

1. What do you mean “an image”?
2. Why would anyone do that?

In this post, I’ll answer both questions and explore where malware visualization fits among other malware classification techniques, and why it still matters.

A Malware Image

The method developed by Nataraj’s team at UC Santa Barbara remains the gold standard for transforming binary files into images and, more importantly, it still delivers state-of-the-art performance. The core idea is straightforward:

1. Treat the binary file as a raw sequence of bits (0s and 1s).
2. Group them into 8-bit bytes.
3. Map each byte’s value to a grayscale pixel (0 = black, 255 = white).
4. Reshape this linear byte stream into a 2D matrix (your image dimensions of choice).

And just like that, you’ve got your image.

From there, it’s just a matter of deciding how to use it. Most research has focused on malware detection and family classification, leveraging the fact that different malware families leave distinct visual “fingerprints” in these images.

Over the years, researchers have thrown everything at the problem: classical machine learning, deep learning, and especially CNNs, which rode the recent AI hype wave.

But it’s not just CNNs and machine learning models that spot these patterns. While humans can’t easily parse meaning from these pixelated blobs, we can recognize repeating textures and visual motifs. Some images clearly cluster together based on these features. So, something’s definitely there, even if it’s not easy to figure out how to fully decode it.

Why Do It This Way?

So, we know malware visualization can work, but is it actually worth using? To justify its place, it needs to outperform other methods in key areas. Let’s be clear: this isn’t some revolutionary silver bullet for malware detection. It has flaws (plenty of them) and standalone, it often underperforms compared to the best traditional techniques, notwithstanding what certain papers claim.

Yet, malware visualization still carves out a niche in the broader landscape of analysis. Why? Because it offers unique advantages that other approaches struggle to match, some of which may grow even more critical as the field evolves.

A standard dataset used not only in malware visualization but in malware classification in general is the Big2015 dataset developed by Microsoft in 2015. The best malware visualization models obtain more than 99% accuracy on the dataset, outperforming major machine learning technique that doesn’t use visualization.

Automatic Classification

Let’s start with a major advantage shared by many machine learning, and especially deep learning, techniques: ease of application. I don’t mean the method itself is simple to implement, but once the system is in place, classification requires no expert intervention. You feed the malware image into the model, and it outputs a classification with reasonable confidence and accuracy. The beauty of black-box neural networks.

Of course, a malware analyst can still verify the result, using the model’s prediction as a (likely correct) starting point. This speeds up the workflow significantly. But this convenience isn’t guaranteed. Black-box models like CNNs are notoriously hard to interpret (more on that later).

CNNs aren’t the only option, though. Traditional machine learning can also be applied, often with more explainable feature extraction methods compared to end-to-end deep learning. This is a general trade-off between classic ML and deep learning: while ML offers better interpretability, it requires careful feature engineering, demanding expert input.

The problem? In malware visualization, the features are visual: patterns that humans struggle to define manually. That’s why, despite their opacity, black-box approaches remain the preferred choice today. They’re simpler, demand less from analysts, and generally deliver better performance.

Resilience to Obfuscation Techniques

Malware visualization introduces a fundamentally different perspective for analyzing malware, one that naturally defeats certain evasion tactics. To understand why, let’s compare it with traditional approaches like the EMBER model. EMBER relies on a random forest classifier processing carefully engineered static features (PE header fields, .data section strings, etc.). While effective, these specific signatures create clear attack surfaces. Malware authors can (and do) easily manipulate these known features to evade detection.

Visualization models face similar adversarial challenges, but with a key difference: successful attacks require manipulating the actual binary content in ways that meaningfully alter its visual representation. Since the model interprets the entire binary holistically, trivial modifications (like changing a few bytes) typically won’t significantly impact the classification. The attacker would need to substantially reorganize the executable’s structure - a far more complex undertaking, especially at scale.

Important Note: Yes, packing remains an effective obfuscation method against visualization, as it is against most automatic analysis techniques. However, this can be effectively mitigated by including packed samples (using common packers like UPX) in the training dataset. Research has shown this approach maintains strong detection accuracy even against packed malware, making visualization surprisingly resilient where traditional signature-based methods often fail.

Privacy Benefits

One often-overlooked advantage of malware visualization is its inherent privacy-preserving quality. It acts as a form of fuzzy hashing that enables classification without direct access to the original file contents. While this might seem irrelevant (after all, how private can a binary file be?), the reality is more nuanced.

First, modern security solutions must analyze all file types, not just known to be executables, as malware can hide anywhere. Second, even binary files can reveal sensitive patterns about their origins or purpose through detailed analysis. By transforming files into visual representations, we create a mostly irreversible abstraction layer that helps prevent such profiling.

Interestingly, this wasn’t a design goal for the original researchers. But in today’s geopolitical climate, where organizations might hesitate to allow (foreign) antivirus software with high system privileges, this characteristic becomes increasingly valuable. The ability to analyze threats through transformed representations like images could lay the groundwork for more privacy-conscious security solutions in the future.

Designing a Malware Visualization Pipeline in Detail

While building an end-to-end malware visualization pipeline may appear straightforward, each design decision carries important trade-offs. Developers should understand these key considerations, even at a high level to be able to use and understand a malware visualization system.

1. Starting Information

The initial choice of what information to visualize represents the most important, yet frequently overlooked, decision in the pipeline. While the possibilities for data-to-image conversion appear limitless, ranging from raw binaries to API call sequences and entropy measurements, this foundational selection critically influences every subsequent stage of analysis.

A fundamental principle often disregarded is the necessity of comparable baselines. There exists little value in comparing a model trained on dynamic behavioral traces, such as API call patterns, against one utilizing static binary visualizations.

The type of input data implicitly dictates the universe of detectable patterns. Raw binary visualizations preserve structural artifacts that differ fundamentally from the statistical signatures revealed by entropy mappings. Each approach illuminates different aspects of potential malware behavior, meaning the choice effectively predetermines what threats the system can and cannot recognize.

These initial decisions ripple through the entire pipeline, constraining architectural choices and deployment possibilities. A neural network optimized for processing binary-derived images may prove woefully inefficient when applied to visualized behavioral logs. The uncomfortable reality is that many claimed performance breakthroughs evaporate when examined through the lens of consistent input methodologies. Rigorous evaluation demands either identical input types for benchmarking, or explicit accounting for how input formats influence detection capabilities.

Image Generation

This critical stage builds directly upon our initial input selection, demanding careful consideration of how to translate abstract data into visual representations. These transformation choices often define a model’s fundamental capabilities and limitations, making this arguably the most consequential design phase.

The process involves two interdependent decisions that shape the resulting visualization: spatial arrangement and color mapping. Of these, spatial organization proves most impactful for analysis.

Spatial Representation
The minimal requirement involves projecting data into at least two dimensions, typically as a 2D matrix. More sophisticated approaches might employ three-channel representations (like RGB) to encode additional dimensions through color. While specialized techniques exist for particular data types—entropy histograms, hash-based mappings, or other domain-specific transforms—the most common approach for binary files utilizes space-filling curves. These mathematical constructs provide systematic methods for flattening sequential data into two-dimensional space while preserving certain locality properties.

Color Encoding
While generally less influential than spatial arrangement, color mapping presents interesting opportunities. The simplest approach applies post-hoc colormaps to grayscale representations, offering minimal analytical benefit. More innovative techniques leverage RGB channels to encode distinct data aspects—for instance, separating different file sections or alternative representations across color channels. However, most source data lacks inherent color associations, limiting this technique’s practical utility.

Dataset Implications
These combined choices ultimately generate the training corpus. The field currently relies heavily on two legacy datasets—Big2015 and MalImg—despite their known limitations and aging samples. Their continued use as benchmarks highlights both the challenges of curating new datasets and the field’s need for standardized evaluation frameworks.

3. Model Selection and Feature Extraction

While feature extraction and model architecture could warrant separate deep dives, their inherent interdependence makes it practical to discuss them together here. The core challenge lies in selecting an analytical approach that effectively leverages the visual representations we’ve created.

The Dominant Paradigm
Current practice largely follows two paths:

1. End-to-end CNN implementations that handle feature extraction and classification simultaneously
2. Hybrid approaches where CNNs serve as feature extractors for traditional machine learning models (e.g., SVMs)

A Critical Perspective on Current Research
Much published work in this space demonstrates what might charitably be called “application engineering” rather than genuine innovation. The field has largely contented itself with repurposing existing computer vision architectures—taking off-the-shelf CNNs that perform well on natural images and applying them to malware classification with minimal adaptation. Few researchers attempt to develop models specifically tailored to the unique characteristics of malware visualizations, instead opting to combine popular image recognition architectures in hopes of achieving marginally better accuracy scores.

This isn’t to suggest that custom architectures would necessarily yield dramatic improvements—the fundamental visual patterns in malware may not differ enough from natural images to warrant completely novel approaches. However, the field’s current trajectory risks becoming an endless cycle of benchmarking slightly modified vision models against the same aging datasets.

The Path Forward
Model tuning and evaluation remain essential for transitioning from academic research to practical applications. But we might benefit from exploring alternative directions:

• Developing architectures that better capture the structural artifacts particular to executable files
• Creating evaluation frameworks that measure real-world robustness rather than just accuracy on curated datasets
• Investigating how visualization choices interact with model architecture decisions

The current emphasis on model comparisons, while necessary, feels increasingly like rearranging deck chairs when larger questions about methodology and evaluation remain unaddressed.

4. Evaluation and Interpretation

The final and crucial phase involves rigorously assessing model performance. While standard machine learning considerations about metric selection (accuracy, precision-recall, etc.) apply here as they would in any classification task, malware visualization presents unique evaluation challenges that deserve special attention.

Performance vs. Practicality Trade-off
These models frequently achieve impressive accuracy scores compared to other automated classifiers, but this comes with significant computational costs. The most performant architectures often demand substantial resources—both in processing time and hardware requirements—creating real-world deployment challenges that bench-marking papers frequently overlook.

The Growing Imperative of Explainability
Beyond traditional adversarial testing, the field now faces increasing demands for model interpretability. While we can borrow techniques from general computer vision (like saliency maps or attention mechanisms), these approaches often prove inadequate for malware analysis. The visual artifacts that indicate malicious intent in binary-derived images differ fundamentally from patterns in natural images, requiring domain-specific interpretation methods.

A Call for Specialized Techniques
This represents perhaps the most promising research direction for advancing malware visualization. Rather than simply applying generic explainability tools, we need to:

1. Develop visualization-specific interpretation frameworks that account for executable file structures
2. Create evaluation standards that measure how well explanations align with malware analyst intuition
3. Establish whether certain visualization methods inherently lend themselves to better interpretation

Progress in these areas could elevate malware visualization from a research curiosity to a mainstream analysis technique. The models that will ultimately succeed in real-world deployment won’t just be the most accurate—they’ll be the ones that can effectively communicate their reasoning to security analysts while maintaining reasonable computational efficiency.

The Future of Malware Visualization

While malware visualization may not be the ultimate solution for detection, its unique advantages ensure it remains relevant in the security landscape. The technique’s resilience to certain evasion tactics and its inherent privacy-preserving qualities offer distinct benefits that conventional methods struggle to match.

What makes the field particularly promising is its untapped potential in model interpretability. Rather than treating these visualizations as mere inputs for black-box classifiers, we could develop specialized techniques to extract meaningful forensic artifacts from the images themselves. This approach could bridge the gap between machine learning and human analysis, creating more transparent and actionable results.

Major industry players have already recognized this potential - Google’s use of visualization in VirusTotal and CrowdStrike’s research investments demonstrate real-world applicability. Although academic interest has plateaued, the foundation exists for meaningful advancement. Future progress will likely come from:

1. Developing visualization-specific interpretation methods
2. Creating hybrid approaches that combine visual analysis with other techniques
3. Addressing computational efficiency for enterprise-scale deployment

Malware visualization may never replace traditional analysis, but its unique perspective on binary files continues to offer value. As attackers evolve their tactics, having this additional lens for examination could prove increasingly valuable in the defensive arsenal.