Episode 39 — Extract static malware features that travel well
In Episode 39 — Extract static malware features that travel well, the focus is on a type of analysis that often feels less exciting than watching malware run, but delivers durable value when you need indicators that can be shared and reused. When you are dealing with a surge of samples, you cannot execute everything, and even when you do, behavior can be shaped by environment, timing, and anti-analysis checks. Static features give you a different kind of leverage. They let you inspect a file without running it and still pull out characteristics that persist across variants. Those characteristics are the ones that travel well, meaning they can be communicated to other teams, used in detection logic, and applied consistently across environments. The goal is not to replace behavioral analysis, but to complement it with features that remain stable even as attackers change surface details.
Static analysis is the process of examining a malware file without executing the code on a system. Instead of watching what the program does, you inspect what it contains and what it appears designed to do. This includes examining headers, embedded data, imported functions, sections, metadata, and patterns that reveal intent or lineage. Static analysis is especially useful early in triage because it can be done quickly and safely, and it often reveals whether a sample is worth deeper study. It also helps you identify whether a file is packed, obfuscated, or otherwise engineered to resist analysis, which changes your expectations about what you will see at runtime. The most important mindset here is that static analysis gives you evidence about design and construction. It does not always give you evidence about the exact runtime outcome in a specific environment.
One of the most practical static techniques is extracting unique strings from the binary that might indicate a specific author habit, campaign tag, or infrastructure reference. Strings can include hardcoded domain names, paths, registry keys, command line fragments, user agent patterns, error messages, or internal labels left behind by developers. Some strings are obvious, and others are hidden or encoded, but when you find a distinctive string that repeats across samples, it becomes a strong linking artifact. Strings can also hint at tooling choices, such as libraries used for encryption or network communication. The important part is selecting strings that are meaningful and uncommon, not generic fragments that appear in many legitimate programs. When you choose well, strings become stable indicators that can support both detection and clustering. When you choose poorly, strings become noise that wastes time.
This is also why you should avoid relying on file names, which can be changed easily by an attacker every time they distribute a sample. File names are often misleading, either because attackers deliberately choose names that blend in or because delivery mechanisms rename files automatically. Even when a file name looks suspicious, it is rarely a durable identifier. Static analysis aims to find attributes the attacker cannot change as easily without changing the program itself. That might include internal strings, compile time artifacts, or consistent structural patterns. A file name can still be useful as context, especially if it reflects the delivery method, but it should not carry your conclusions. Treat it as a hint, not a signature.
The import table is another rich source of static insight because it shows which system functions the program intends to use. Imports can reveal whether the malware is designed for network communication, credential access, process injection, registry manipulation, or file encryption. You are not watching execution, but you are seeing what capabilities the program expects to call into the operating system. A sample that imports networking and cryptographic functions suggests different goals than one that imports functions related to service creation or remote execution. Import patterns can also reveal whether a sample uses common libraries or custom implementations. Over time, consistent import sets can act as travelable features, especially when paired with other static artifacts. They can also help you decide what runtime behaviors to look for if you later execute the sample safely.
Imagine reading the text strings inside a program and discovering a hidden message, a hardcoded URL, or a campaign label embedded by the developer. That moment illustrates why static analysis can reveal information that runtime observation might miss. Some malware only reveals certain behaviors under specific conditions, but strings can expose planned behavior and hidden relationships immediately. A hardcoded domain can point to command and control infrastructure. A path can reveal where the malware expects to install itself. A configuration label can suggest how operators manage campaigns. Even when strings are obfuscated, their presence can hint that the malware carries configuration data internally rather than retrieving it dynamically. This kind of discovery is often a shortcut to infrastructure pivoting and link analysis. It turns a standalone file into a clue that connects to the broader operation.
A useful metaphor is to think of static features as the permanent physical characteristics of a piece of digital malware. Just like a physical object has material properties, a binary has structural and content properties that reflect how it was made. Attackers can repaint the outside, but changing internal structure requires effort and can introduce bugs. That is why certain static traits persist across versions, especially when an actor uses the same build process, libraries, or templates repeatedly. This metaphor also helps you understand the limits of static indicators. Some features are truly durable, like certain structural patterns, while others are superficial and easily changed. The skill is learning which physical characteristics matter and which are just cosmetic. When you select the right ones, they travel well across environments and over time.
Identifying the file type and the architecture the sample was designed to run on is another key early step in static analysis. The file format can tell you whether it is a portable executable, a script, a document with embedded code, or a different artifact type entirely. Architecture tells you whether it is built for a specific platform, which affects what systems are at risk and what behaviors are plausible. These details matter operationally because they help you scope impact quickly and avoid wasting time trying to run the sample in an incompatible environment. They also help you choose the right analysis tooling and the right sandbox environment if you proceed to behavioral observation. In a busy investigation, these simple determinations can prevent hours of confusion. They are foundational facts that travel well because they are properties of the file itself.
Static features are especially valuable for creating stable indicators that can be shared with other teams because they are easy to communicate and reproduce. A shared indicator should be something another team can extract reliably from a file and use in a consistent way. Good travelable indicators include specific uncommon strings, distinctive section patterns, consistent import sets, and other structural markers that survive minor variations. When you share these features, you are giving others a way to recognize related samples without needing your entire investigative context. This is what turns individual analysis into collective defense. It also supports collaboration because teams can compare findings using a common vocabulary of features. The goal is shared visibility and faster recognition of variants.
Look for unique constants or hardcoded encryption keys because these can be particularly strong static artifacts when they appear. Constants can include configuration markers, magic values, or identifiers used by the malware to validate its own data. Hardcoded keys or key material, when present, can reveal how the malware protects its communications or its stored data. These elements are not always available because many actors avoid leaving such artifacts in clear form, but when they exist, they can be highly diagnostic. A reused constant across samples can suggest shared code or shared build processes. It can also provide opportunities for deeper analysis, such as decrypting configuration data or recognizing command and control patterns. Even if you do not exploit the constant directly, recognizing it as a stable feature can strengthen clustering and attribution hypotheses when combined with other evidence.
Static analysis is also a fast way to triage a large number of new malware samples, which is often the practical reality in busy environments. When you receive many suspicious files, you need a method to separate the ones that are likely related from the ones that are unrelated. Static features help you do that by providing quick comparison points. You can group files by file type, architecture, packer characteristics, or shared string artifacts, and then decide which group deserves deeper behavioral study. This triage approach also helps you identify duplicates or near duplicates, which saves time by preventing repeated analysis of the same thing. It keeps your workflow efficient and consistent, especially when multiple analysts are involved. Static analysis is often the first pass that makes the rest of the pipeline manageable.
File hashes play a role here as well because they are unique fingerprints that allow you to track a specific file across your environment. When you use a hash, you can search across endpoints, logs, and repositories to see where the exact artifact appeared. This is extremely useful for scoping and containment because it tells you how widely the file has spread. The limitation is that hashes change when the file changes, and attackers can easily modify a binary to produce a new hash. That means hashes are excellent for identifying exact matches but weak for identifying families or variants. In practice, you use hashes for precise tracking and you use other static features for travelable recognition across versions. Treating hashes as one tool among many keeps your approach balanced and effective.
Another valuable static exercise is identifying the compiler used to build the malware, because build artifacts can reveal actor habits and tooling choices. Compilers and build environments can leave consistent fingerprints in metadata, section structure, or embedded patterns. These fingerprints can suggest whether the malware was produced by a professional development workflow, a commodity builder, or a specific toolchain. Over time, consistent compiler signals across samples can support clustering and help you distinguish between unrelated families that share superficial behaviors. The key is to use compiler identification as a supporting signal rather than as a decisive factor, because build fingerprints can be spoofed or altered. Still, when combined with strings, imports, and other artifacts, compiler habits can become part of a coherent profile. This kind of profiling is especially useful when you track multiple campaigns over time.
The most important outcome of static feature extraction is that it gives you durable building blocks for broader analysis. Strings can lead to infrastructure pivoting, imports can suggest expected behaviors, file type and architecture can scope exposure, constants can link variants, and hashes can track exact spread. Each of these elements can be documented and shared, and each can become part of a larger case narrative. Static features support clustering because they provide concrete attributes that can be compared across samples. They also support hypothesis formation because they suggest what the malware is designed to do and what evidence should appear at runtime. When you treat static analysis as the foundation rather than the finish line, it becomes a powerful amplifier for the rest of your investigative workflow.
Static analysis also has a quality control role because it helps you avoid being misled by the most visible parts of a file. Attackers often try to distract defenders with misleading names, decoy strings, or superficial markers meant to mimic other groups. A disciplined static approach looks past those distractions and focuses on features that are hard to change without breaking functionality. It also encourages humility, because you learn to separate what you can state confidently from what remains uncertain until runtime observation. This prevents overconfident labeling and keeps your conclusions tied to evidence. Over time, teams that practice disciplined static analysis produce more consistent indicators and fewer false assumptions. That consistency improves collaboration and speeds up response.
Conclusion: Static features are durable so extract the strings from a suspicious file today. When you pull strings, inspect imports, identify file type and architecture, and note distinctive constants and build fingerprints, you create a set of features that travel well across teams and investigations. These features support rapid triage when sample volume is high, and they provide stable indicators that help you recognize related artifacts even when attackers change surface details. Use file hashes to track exact matches in your environment, but rely on richer static features to connect variants and campaigns. When you document what you find and share the most unique artifacts, you turn one sample into a broader defensive advantage. Take a suspicious file, extract its strings, and capture the most distinctive ones, because that is often the fastest path to durable insight and better detection.
