What is malware detection?
Malware detection is a security process that involves finding and eliminating malicious software, also known as malware, from enterprise IT and cloud environments. With malware attacks (and new cloud-native variants) on the rise, malware threat detection and response has to be a core aspect of your cloud security program.
Malware is a term that refers to a range of different threats like botnets, viruses, ransomware, Trojans, and spyware. The role of malware detection is to make sure that these types of malware are found and dealt with before they cause serious problems. If they aren’t effectively addressed, malware attacks can spiral out of control and cause long-term damage. The DollyWay campaign, which has been active since 2016, is an example of how malware attacks can get out of hand.
Because there’s a continuous influx of new strains of malware, it’s very important for you to keep your malware threat detection and response techniques fresh. In the past, more primitive malware was dealt with by antivirus programs. Now, with the advent of AI-driven attacks and an increase in polymorphic and metamorphic strains, which are basically malware that can change to avoid detection, you need a whole new approach to malware threat detection and response.
The State of Code Security Report [2025]
The State of Code Security Report 2025 found that malicious GitHub Apps and overly permissive repository settings are among the biggest risks to modern software development. Attackers exploit these gaps to insert malicious code.
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How does malware detection work?
Before we get into the nitty-gritty of malware detection, we need to first talk about malware signatures. Malware signatures are basically a sample of code that can help you identify a specific strain of malware.
To get malware signatures, you need to harvest samples from malware and extract and decode its features, which may include hash values, file sizes, functions, and behaviors. These signatures are then kept in a database to support malware detection mechanisms. A malware detection tool can check findings against the database of known signatures and alert you if any are identified.
Identifying and working with known signatures is a tried-and-tested model, but it’s not without limitations. Since this model relies solely on known signatures, it often misses novel threats—especially zero-day attacks and polymorphic or metamorphic malware that change their appearance to evade detection.
And that’s where heuristic analysis can be useful. Heuristic malware analysis goes beyond known malware signatures by analyzing static code structures and logic for suspicious traits, even without executing the code. In contrast, behavioral analysis monitors how software behaves at runtime to catch malicious activity based on deviations from expected patterns.
Since malware attacks are being deployed at a greater speed and scale than anyone could’ve imagined, detection engineering teams are relying more and more on AI and ML to counter these attacks. With the right AI technologies and ML algorithms, security teams can process vast volumes of cloud telemetry to detect unusual behavior. However, ML models must be carefully tuned to avoid false positives and alert fatigue—especially in dynamic, high-volume cloud environments.
Signature-based detection vs. behavioral analysis: A snapshot
Next, we’ll move on to malware detection tools and techniques. But first, let’s quickly summarize the two broad types of malware detection: signature-based detection and behavioral analysis.
Signature-based detection: This type of detection involves scanning cloud files and applications to see if they contain known signatures. Detection engineering teams can keep adding newly discovered malware signatures into their databases through manual and automated means. But the issue with signature-based detection is that zero-day attacks and polymorphic and metamorphic malware can bypass these systems.
Behavioral analysis: This type of anomaly detection technique focuses on monitoring traffic and behaviors so that deviations from the baseline that might point to a malware attack can be identified. Behavioral indicators of malware include performance lags, unexpected surges in network traffic, suspicious logins or API calls, and spikes in cloud resource usage. What makes this style of malware detection stand out is that it doesn’t rely on known signatures.
What are the best tools for malware detection?
Like every part of your cloud security program, the effectiveness of malware detection wholly depends on the kinds of tools you use. Let’s take a look at some of the top malware detection tools you can choose from:
YARA: YARA is an open-source and multi-platform tool that can help malware researchers find, learn about, and classify various strains of malware. YARA enables you to group certain types of malware and create descriptions, also known as “rules.”
Wireshark: Wireshark is an open-source packet analyzer (also known as a network protocol analyzer) that allows you to capture packets, which are basically small batches of data, from a network and conduct a root cause analysis. These analyses can be done either in real time or in a controlled environment.
Cuckoo Sandbox: An open-source automated malware analysis tool, Cuckoo Sandbox lets you run and experiment with malware in isolated and controlled environments. With this tool, you’ll be able to study malware behavior and figure out the ideal incident response plan to remediate it.
VirusTotal: VirusTotal is a no-cost online service that lets you scan URLs and files to check for malware like worms and Trojans. An aggregator of numerous scanners and services, VirusTotal is a very useful tool for analyzing malware. It’s also a pretty comprehensive source of malware threat intelligence.
Ghidra: Ghidra is an open-source reverse engineering framework that can help you break malware down to its source code. By doing so, you can better understand how a strain of malware works and what cloud incident response playbooks you can establish to tackle it in real-world scenarios.
Volatility Framework: The Volatility Framework is a free, Python-powered tool for memory forensics that helps you grab and analyze volatile memory. This framework is good for understanding the inner workings of malware and designing strong incident response plans to mitigate it.
In addition to these powerful open-source tools, there are many proprietary malware detection tool options you can choose from. But before you make a decision about what kind of malware detection tool you want, just remember that you need to prioritize real-time malware threat detection and response capabilities—considering the volume of malware attacks businesses have to deal with today, real-time malware threat detection and response is the only way to prevent breaches.
What are some effective malware detection techniques?
Earlier, we took a look at some broad categories of malware detection techniques like signature-based detection and behavioral analysis. Now, let’s dig into a few specific techniques that can help you find and mitigate malware:
Sandboxing: Sandboxing is a technique that involves executing malware in a temporary, isolated environment—often a container or virtual sandbox—to safely observe its behavior without risking live cloud systems.
Honeypots: Honeypots are basically decoy environments that you can set up in parallel to your actual environments. The point of honeypots is to create an alluring and realistic target to invite a malware attack. When the attack occurs, you can trap the malware, analyze it, and use the knowledge to design resilient fortifications and cloud incident response plans.
Allowlisting: Allowlisting involves establishing a list of approved software applications that can run within your cloud environment. By reducing the number of potential attack vectors (entryways) in your cloud, allowlisting significantly cuts down on the possibility of malware attacks and lets you easily detect anything outside the norm.
Blocklisting: Blocklisting, the opposite of allowlisting, involves establishing a list of banned applications. This is a safe and effective technique to mitigate risks associated with known malware threats.
Anomaly detection: Anomaly detection involves using AI and ML capabilities to establish a security baseline and automatically detect unexpected or suspicious patterns that deviate from it. Anomaly detection is a good way to spot malware but runs the risk of raising false alarms.’
Threat detection and SIEM
The techniques we’ve discussed can go a long way toward keeping your cloud safe. But when it comes to malware detection, threat intelligence feeds are the cherry on top because they can significantly improve detection accuracy. You have many threat intelligence feeds to choose from, but it’s a good idea to start with our own Cloud Threat Landscape to understand the nuances of malware in the cloud.
Another way to make the most of your malware detection tools and techniques is to unite them with SIEM tools like Splunk, AWS Security Hub, IBM QRadar SIEM, and Datadog. These tools enrich your malware detection capabilities by correlating raw findings with contextual data—such as IAM role changes, suspicious login activity, and system drift—to prioritize real-world threats that could affect your organization.
In other words, you’ll hunt down malware-related threats that might actually affect your organization’s cloud operations and sensitive data. This way, you avoid wasting time on threats that probably won’t make a big difference.
How Wiz Defend helps you detect malware in the cloud
To effectively detect malware in the cloud, you need a solution purpose-built for modern, dynamic environments—not one retrofitted from legacy endpoint models. That’s where Wiz Defend excels.
Wiz Defend combines signature-based, behavioral, and anomaly-based detection to identify malware across build, deploy, and runtime phases. Here’s how:
YARA-powered signature detection: Wiz Defend supports both custom and built-in YARA rules to identify known malware across container images, workloads, and storage.
Behavioral anomaly detection: Wiz continuously monitors workload behavior, surfacing indicators like suspicious process execution, network spikes, or abnormal API usage—classic signs of malware evasion or persistence.
Runtime threat detection: Wiz provides agentless runtime visibility and flags memory injection, credential access, and other in-memory behaviors associated with malware—even without kernel-level agents.
Cloud-native IOC enrichment: Wiz correlates malware detections with cloud-native context, such as excessive permissions, external exposure, access to sensitive data, and privilege escalation pathways.
SIEM and SOAR integrations: Wiz Defend seamlessly integrates with tools like Splunk, QRadar, and Datadog to enrich threat detection and accelerate incident response.
Security Graph prioritization: Wiz’s Security Graph shows how malware findings intersect with exposed resources or blast radius, so you can prioritize what matters most.
Automated response workflows: Wiz supports automated triage, remediation suggestions, and CI/CD pipeline fixes, speeding up both detection and resolution.
With malware constantly evolving, Wiz Defend helps you reduce mean time to detect (MTTD) and mean time to respond (MTTR)—all with fewer false positives and greater visibility.
Want to see how it works? Request a demo and explore how Wiz Defend simplifies cloud detection and response.
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