Malware can enter and wreck almost any computer system. It’s not a static category—new types of malware are constantly being created. Malware analysis pinpoints malicious software to understand how it works and what to do when it’s compromised a team.
In practice, there are a few approaches to malware analysis—static analysis, dynamic analysis, and code-level reversing—that uncover what a sample does and which defenses will stop it. When you bake malware analysis into your security program, you build stronger detection behaviors and faster response times because you know exactly what indicators to watch for. This guide explores what exactly malware analysis is, stages and use cases, and a few tools analysts use to guide their studies.
What Is Malware Analysis?
Malware analysis is the practice of examining malicious files, URLs, and code to understand what they do and how they cause harm. These samples can show up in many ways: phishing emails, drive-by downloads, and compromised websites or infected media, for example.
Analysts study malware to uncover hacker and user behaviors. For development teams, they can extract IoCs and pinpoint weaknesses the malware exploited. Understanding different types of malware also helps analysts predict likely behaviors and choose the best remediation approach. In many cases, malware analysis links a sample back to a broader campaign or even to a known advanced persistent threat (APT) group, which gives defenders important context about the attacker(s) behind the malware.
Security operation center analysts use malware analysis to find indicators of compromise (IoCs) faster, and incident responders rely on the practice to guide containment. Developers and AppSec engineers also use findings to harden code and close off weak points.
There are several approaches to the analysis, but the goal remains the same: to turn a suspicious sample into clear, actionable intelligence for defenders.
Malware Analysis Techniques
An analysis of malware can take many forms, and each sheds light on a different way threats operate. From fixed reviews to full reverse engineering, these techniques give analysts the insight they need to detect, respond, and strengthen defenses.
Static Analysis
During static malware analysis, analysts examine a sample without running it, like a moment frozen in time. Analysts pull metadata such as strings, hashes, and headers to quickly spot red flags and extract IoCs. It’s a broad approach, but heavily packed samples can hide what’s really happening.
Dynamic Analysis
In dynamic analysis, analysts detonate the sample in an isolated sandbox or virtual machine and watch what it actually does. What processes does the sample spawn? What files does it drop? Does it touch any registry keys or make any network calls? Observing the sample in a contained environment reveals runtime behavior and command and control (C2) patterns, making it more practical for remediation, but some malware tries to detect or evade sandboxes to skirt past dynamic analyses.
Reverse Engineering
To reverse engineer malware, analysts disassemble or decompile the binary file to read its logic. Then, they carefully debug it and uncover encryption routines, unpacking stubs, and anti-analysis tricks. It takes significantly more time and skill than static or dynamic analysis, but pays off by revealing the deepest insights.
Memory Analysis
For rootkits and packed malware that only reveals itself at runtime, memory analysis can be a useful technique. Analysts look at memory captures to find injected code, unpacked payloads, hooks, and other artifacts that may never touch the disk.
4 Stages of Malware Analysis
Malware analysis usually unfolds in stages, starting with automated scans and moving toward deeper, more hands-on techniques like reverse engineering. Here are the four most common steps analyzers use to identify and study malware.
1. Fully Automated Analysis
This stage relies on automated tools to quickly process large numbers of suspicious samples. The goal is efficiency at scale: Automated engines flag potential threats and provide quick insights without requiring any serious effort. It’s great for triage, but the tradeoff for its speed is a limited depth, which can be harmful if the malware uses evasion or obfuscation to go unnoticed.
2. Static Properties Analysis
Once an automated analysis has concluded, analysts examine the sample without running it. They look at properties like headers and embedded resources to extract IoCs. These checks can quickly signal whether a sample warrants a closer look.
3. Interactive Behavior Analysis
In this stage, analysts run the malware in a controlled lab and interact with it, often with dynamic analysis techniques. They monitor the processes, file changes, and network traffic, which they often combine with memory analysis. This hands-on approach uncovers behaviors automation might miss and lets analysts test specific hypotheses.
4. Manual Code Reversing
The most advanced and time-intensive stage involves reverse engineering the malware’s code, which might be written in any number of programming languages. Analysts use debuggers, disassemblers, and decompilers to uncover hidden logic and anti-analysis tricks in the code. Manual code reversing provides the clearest understanding of the malware’s true capabilities and intent.
Malware Analysis Use Cases
Knowing what kind of malware you’re up against is one thing, but teams still need to use the analyst’s data to fix their system. From cutting through noisy alerts to building long-term threat intelligence, here are some of the most common ways security teams put malware analysis to work.
Threat Alerts and Triage
Malware analysis improves alert fidelity by uncovering hidden IoCs. Instead of drowning in false positives, security teams can prioritize alerts tied to real malicious behavior, saving time and cutting down on noise.
Incident Response
During an active breach, malware analysis gives incident responders the details they need to mitigate and remediate the issue: how the sample spreads, which systems it touched, and what persistence techniques it used. With that knowledge, they can contain the threat faster and drive more effective remediation and recovery.
Threat Hunting
Threat hunters, who proactively search for malware within a system, rely on malware analysis to generate leads. Details that analysts find, like unique domains, registry keys, and API calls, become threads hunters can pull on to surface related activity in logs and telemetry that would otherwise stay hidden.
Reverse Engineering and Research
Deep malware analysis feeds the research side of cybersecurity. By reverse engineering samples, researchers can uncover new encryption methods and C2 infrastructure. This not only expands detection rules and better cybersecurity overall, but also supports attribution—and sometimes even links a malware family to a campaign or threat actor.
Threat Intelligence Sharing
The value of malware analysis extends beyond the analyst's workstation. Malware analysis outputs like IoCs, behavioral patterns, and tactics, techniques, and procedures (TTPs) are fed into threat intelligence and management platforms or shared across the security community. That collective intelligence turns isolated findings into actionable intel for other teams, strengthening defense across organizations.
What Tools Are Available for Malware Analysis?
Malware analysts don’t act alone. Here are some widely used malware analysis tools that give teams widespread visibility:
- System Informer (formerly Process Hacker): This open-source monitoring tool lets analysts observe processes running in real time. When malware executes, you can see exactly which processes it spawns or manipulates.
- PEStudio: PEStudio inspects Windows executable files without running them. It highlights suspicious properties like imports, embedded strings, or hardcoded IPs. Analysts use it to safely collect IoCs and decide whether a sample needs further investigation after an automated detection.
- Ghidra: Originally developed by the United States National Security Agency (NSA), Ghidra is a reverse engineering suite that disassembles and decompiles malware into code humans can read. It’s a go-to for unpacking obfuscated samples and uncovering the logic behind complex malware.
- Cuckoo Sandbox: Cuckoo provides a virtualized sandbox to safely run malware and log everything it does, from file modifications to network traffic. This view helps analysts see how malware naturally behaves without the risk of accidentally infecting production systems or spreading it across networks.
Defend Against Malware With Legit Security
Malware analysis isn’t the only way to intercept and understand malware in your system. Legit Security enhances malware analysis by embedding real-time software supply chain security into your development workflows. By monitoring every stage of the pipeline, Legit Security blocks potential security issues tied to open-source dependencies and third-party packages before they reach production.
Whether it’s preventing a software supply chain attack, catching risks tied to open-source software, or spotting the next major open-source malware attempt on platforms like GitHub before it happens, we give your teams the visibility and guardrails they need to stay ahead of attackers.
Prioritize resolving security vulnerabilities and stopping malware before it has the chance to enter your system. Schedule a demo with Legit Security for maximum security visibility.
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