Reviewed and updated in October 2026.
Signature-based antivirus software alone no longer protects against today's attacks. In a hyperconnected world, one wrong click can spread to thousands of machines: within days of its outbreak in May 2017, the WannaCry ransomware had hit more than 200,000 victims in 150 countries, according to Europol. Traditional security needs a sacrificial lamb: someone has to be attacked before everyone else can be protected.
Behavior-based security detects threats by what a program does, not by what it looks like. Instead of comparing files with a list of known malware signatures, it learns how a system normally behaves and flags or stops activity that deviates, such as a process that suddenly encrypts large numbers of files. That lets it catch new and modified malware for which no signature exists yet.
Someone has to get infected so others can be saved later. Signature-based antivirus software checks the files on a computer and compares their hash, or signature, with a list of known malicious files such as trojans and viruses, called definitions. Vendors update that list as soon as a new threat is identified and push the update to every computer running their software. Typical actions are blocking the file from executing, quarantining it or deleting it, in the hope that no damage has been done yet.
This is reactive security. It only protects against known threats, and a solution is only as good as its definitions are complete and current. Every computer also has to stay up to date, which usually means being online. And because detection relies on a file hash, a single change to a file creates a new hash that the software no longer recognizes, while the threat remains. More and more malware uses polymorphic code that changes its appearance to stay undetected long enough to do real damage.
Behavior-based threat defense takes a different approach. Instead of relying on a list of known malicious files, the software watches what happens on the system and looks for changes in behavior that could indicate a threat. It learns how the system behaves under normal conditions: which processes run, how they interact, which files they access and how. Deviations from that baseline are flagged, and a whole chain of attack involving several processes and files can be stopped while it unfolds.
That protects not only against known threats but also against unknown ones, including attacks that exploit zero-day vulnerabilities for which no patch or signature exists yet. Why unpatched vulnerabilities are so dangerous is the subject of Security Breach: Patch or Clash.
Most products ship with a curated set of known suspicious behaviors and let customers define their own policies for what is and is not allowed in their environment. Because the detection model often runs locally on the device, many of these agents keep protecting a device while it is offline; cloud lookups and regular updates still improve detection.
Watching behavior continuously takes a lot of computing power and a lot of data, which is where artificial intelligence (AI), machine learning (ML) and big data come in.
AI is the field of building software that solves problems once considered a human strength. Machine learning is a part of AI: a system that does not only solve problems it was programmed for, but learns from data and experience how to solve new ones.
Big data means storing and analyzing amounts of data too large for traditional methods. Behavior-based detection needs a lot of it, because a model learns to recognize patterns from examples. The size and variety of the data a model is trained on matter: the more different environments and attacks it has seen, the more patterns it recognizes.
No. Current Endpoint Protection Platforms (EPP) combine signatures for known malware with machine learning and behavioral analysis for unknown threats. Endpoint Detection and Response (EDR) goes further: it records endpoint activity continuously so analysts can investigate and respond to attacks that use legitimate tools. On smartphones and tablets, Mobile Threat Defense (MTD) plays this role. How the three fit together is explained in Demystifying Security: EPP, EDR and MTD.
Behavioral detection also has a cost: it produces false positives, alerts on activity that is unusual but harmless. Policies need tuning to your environment, and someone has to review the alerts. Why human oversight remains essential even with AI-based tools is discussed in Human Oversight in the AI Era: Finding the Right Balance in TDIR and SIEM.
Cyberattacks on public and private infrastructure cause damage, ransom payments, reputational harm and data loss that can affect a business long after the incident. Many of these attacks rely on new, unseen techniques. Predictive, behavior-based protection gives you the chance to detect and stop them instead of waiting for them to happen and doing damage control afterwards. For an overview of how endpoint protection fits into the wider product landscape, read Cybersecurity Products: What's What.
Signature-based security recognizes malware by comparing files with a list of known threats. Behavior-based security watches what programs do and stops activity that deviates from normal, so it can also detect threats that have no signature yet.
It extends it. Current endpoint protection products combine signatures for known malware with behavioral analysis and machine learning for unknown threats.
It can detect and stop attacks that exploit unknown vulnerabilities by recognizing malicious behavior, without needing a signature. It is not a guarantee, so patching and other layers of defense remain necessary.
Because unusual is not always malicious. An admin script or a new application can look like an attack. Tuning policies to your environment and reviewing alerts keeps the false positive rate manageable.
Want to know which endpoint protection fits your environment? ISEC7 advises on endpoint security, from choosing an EDR or XDR platform to Mobile Threat Defense. Contact us.