To authenticate a person’s identity, passive behavioral biometrics analyze unique user behaviors—like how they type, walk, or talk. Behavioral biometrics work passively in the background, as opposed to physiological biometrics which are active and require the user to take specific actions like scan their face or fingerprints to prove their identity.
Passive Behavioral Biometrics analyze a person’s current behavior — comparing the behavior with samples collected in the past to prove their identity. Unlike physiological biometrics like fingerprint scanning, behavioral biometrics run in the background, passively collecting and analyzing behavior samples without interfering with the user’s experience.
The passive nature of behavioral biometrics allows the technology to be applied seamlessly across various use cases:
As an additional layer of authentication for Two Factor or Multi-Factor Authentication (2FA & MFA)
As a secure method of Continuous Endpoint Authentication (CEA).
As a part of Access Management systems.
Passive behavioral biometrics can be used standalone for authentication, or in tandem for higher accuracy in the identity validation process. Examples of passive behavioral biometrics include:
Typing biometrics (aka keystroke dynamics) - looks at an individual’s typing pattern and analyzes the time it takes them to press, release, and move between keys. Learn more about how typing biometrics work.
Mouse dynamics - identifies patterns in user actions with their mouse. These actions refer to sequences of consecutive mouse events that describe the mouse's movement between two screen positions. Researchers identified 8 key types of mouse movements.
The way the user holds their mobile device - looks at the angle at which a user holds their phone and which hand they predominantly use to hold the device.
Gait Recognition Technologies (GRT) - usually rely on a camera to capture human subjects walking within its field of view. Based on the camera-captured information, gait biometric technologies generate data about an individual’s stride patterns—the shape of the person while they walk as well as the dynamics of the person’s walk.
Voice or speech recognition - transcribe speech into text and break down individual sounds found within the audio of an individual speaking. Voice recognition technologies use Natural Language Processing (NLP) and deep learning neural networks to derive meaning from human language and to determine speech patterns.
An example of a user-friendly, privacy-focused passive behavioral biometric solution is TypingDNA ActiveLock which continuously verifies employees’ identities based on how they type on company devices. By comparing the user’s past and current typing pattern samples, ActiveLock can tell at all times if the true authorized user is the one behind a device — a particularly important piece of information to know with the rise of remote work. In case a different typing pattern is detected, for security reasons, ActiveLock takes instant action to trigger a silent alert and lock the device to protect sensitive data.
Curious to see how passive behavioral biometrics authentication is used in Continuous Endpoint Authentication? Learn more about ActiveLock, or download ActiveLock for free for MacOS or Windows using the form below.
