Robert Derby
Senior Security Product Marketing Manager
Why Malware Detection is Important
Malware detection is the process of identifying malicious software or the activity it produces so security teams can investigate, contain, and remove a threat.
Malware is software or firmware intentionally designed or inserted to perform unauthorized or harmful actions. Common examples include ransomware, viruses, worms, trojans, spyware, and backdoors. NIST also uses malicious code as a closely related term. NIST Computer Security Resource Center
Malware detection can happen before a malicious file executes, while it is running, or after its activity becomes visible across endpoints and networks. Because attackers can modify malware, hide it inside legitimate processes, or use trusted system tools to execute malicious commands, modern detection typically combines several techniques rather than relying on one indicator.
How does malware detection work?
Security tools look for evidence that a file, process, communication, or behavior is malicious.
Some techniques ask whether the threat is already known. Others look for characteristics or activities that indicate something suspicious, even when the exact malware has never been seen before.
Where detection occurs also matters. Antivirus and endpoint detection and response (EDR) tools can observe files, processes, memory, and other activity on a device. Network security tools can examine communications between systems and external destinations. Threat intelligence, email security, sandboxes, and other technologies contribute additional evidence.
Together, these perspectives help security teams identify malware at different stages of an attack.
What are the main malware detection methods?
- Signature and hash detection - Known malware can be identified by comparing files or activity against signatures, file hashes, or other known indicators. This approach is effective when defenders already know what to look for, but modified or previously unseen malware may not match an existing signature.
- Threat intelligence - Security tools can compare IP addresses, domains, URLs, file hashes, and other indicators against intelligence associated with known malicious infrastructure or activity. Threat intelligence can provide strong evidence, although attackers frequently change infrastructure and indicators.
- Heuristic analysis - Heuristics examine characteristics of a file, script, or process for patterns associated with malicious software rather than requiring an exact match. This can help identify suspicious code that resembles known threats.
- Behavioral detection - Behavioral methods look at what software or a system is doing. Unusual process activity attempts to establish persistence, unexpected file changes, abnormal communications, or deviations from normal behavior can indicate compromise even when no known malware signature is present.
- Network-based detection - Many malware infections generate network activity. Malware may download additional tools, contact command-and-control infrastructure, scan internal systems, transfer files, move between hosts, or send data outside the organization. MITRE ATT&CK documents adversaries transferring tools through command-and-control channels and other network protocols after gaining access.
Network monitoring can identify known communication patterns and reveal suspicious activity that becomes observable after malware begins operating.
Why are multiple malware detection methods needed?
Malware changes constantly, and different techniques see different parts of an attack.
A known file hash can provide a high-confidence match, but it may not identify a modified version of the same malware. Behavioral analytics can identify unusual activity without knowing the exact malware family, but unusual behavior is not always malicious.
Endpoint evidence may show which process executed. Network evidence can show what that host communicated with and whether related activity appeared elsewhere.
Attackers can also execute malicious commands through legitimate tools such as PowerShell, Python, JavaScript, and operating system command shells rather than relying entirely on a conventional malware executable.
Combining different forms of evidence gives analysts more context for deciding whether an alert represents a real compromise and what needs to be investigated next.
What role does network visibility play in malware detection and investigation?
Once malware communicates, its network activity can reveal information that the original file or endpoint alert cannot provide by itself.
Analysts may need to determine:
- Which external systems the infected host contacted
- Whether additional payloads were downloaded
- Which internal systems communicated with the affected host
- Whether suspicious activity moved between network segments
- Whether data was transferred outside the organization
- What occurred before and after the initial detection
This becomes particularly useful during threat investigation. Finding malware on one system establishes part of the incident. Understanding its communications helps determine its reach and reconstruct what happened.
How NETSCOUT supports malware detection and investigation
NETSCOUT Omnis Cyber Intelligence (OCI) analyzes network traffic using deep packet inspection and analytics performed at CyberStream and vCyberStream sensors.
Omnis Cyber Intelligence uses multiple detection methods, including threat intelligence indicators, IDS signatures, file detections, behavioral analytics, policy violations, and attack-surface monitoring. These methods help with identifying known malware communication patterns, suspicious protocol behavior, and malicious file transfers on supported traffic.
When suspicious activity requires investigation, Omnis Cyber Intelligence also provides historical network evidence. Analysts can examine host communications, trace interactions with internal and external systems, investigate lateral movement, and drill into packet-level information when available.
This gives security teams a critical source of evidence for determining whether malware activity occurred, which systems were involved, how the activity progressed, and what happened around the original detection.