Overview & System Context

Behavioral Biometrics Based User Authentication System: Comprehensive Web-Sourced Technical Investigation Exhaustive Research Dossier · Pure Web-Scraped Content: ~9555 Words · Real Software Screenshots: 3 1. Executive Overview & Technical Background Biometrics are body measurements and calculations related to human characteristics and features. Biometric authentication (or realistic authentication) is used in computer science as a form of identification and access control. It is also used to identify individuals in groups that are under surveillance. Biometric identifiers are the distinctive, measurable characteristics used to label and describe individuals. Biometric identifiers are often categorized as physiological characteristics which are related to the shape of the body. Examples include, but are not limited to fingerprint, palm veins, face recognition, DNA, palm print, hand geometry, iris recognition, retina, odor/scent, voice and shape of ears. Behavioral characteristics are related to the pattern of behavior of a person, including but not limited to mouse movement, typing rhythm, gait, signature, voice, and behavioral profiling. Some researchers have coined the term "Behaviometrics" (behavioral biometrics) to describe the latter class of biometrics. More traditional means of access control include token-based identification systems, such as a driver's license or passport, and knowledge-based identification systems, such as a password or personal identification number. Since biometric identifiers are unique to individuals, they are more reliable in verifying identity than token and knowledge-based methods; however, the collection of biometric identifiers raises privacy concerns.

Universality: Every person using a system should possess the trait. Uniqueness: The trait should be sufficiently different for individuals in the relevant population such that they can be distinguished from one another. Permanence: The manner in which a trait varies over time. More specifically, a trait with good permanence will be reasonably invariant over time with respect to the specific matching algorithm. Measurability (collectability): The ease of acquisition or measurement of the trait. In addition, acquired data should be in a form that permits subsequent processing and extraction of the relevant feature sets.

Performance: The accuracy, speed, and robustness of technology used (see performance section for more details). Acceptability: How well individuals in the relevant population accept the technology such that they are willing to have their biometric trait captured and assessed. Circumvention: The ease with which a trait might be imitated using an artifact or substitute. Proper biometric use is application dependent. Certain biometrics will be more suitable than others based on the required levels of convenience and security. No single biometric will meet all the requirements of every possible application.

The block diagram illustrates the two basic modes of a biometric system. First, in verification (or authentication) mode the system performs a one-to-one comparison of a captured biometric with a specific template stored in a biometric database in order to verify the individual is the person they claim to be. Three steps are involved in the verification of a person. In the first step, reference models for all the users are generated and stored in the model database. In the second step, some samples are matched with reference models to generate the genuine and impostor scores and calculate the threshold. The third step is the testing step. This process may use a smart card, username, or ID number (e.g. PIN) to indicate which template should be used for comparison. Positive recognition is a common use of the verification mode, "where the aim is to prevent multiple people from using the same identity".

Second, in identification mode the system performs a one-to-many comparison against a biometric database in an attempt to establish the identity of an unknown individual. The system will succeed in identifying the individual if the comparison of the biometric sample to a template in the database falls within a previously set threshold. Identification mode can be used either for positive recognition (so that the user does not have to provide any information about the template to be used) or for negative recognition of the person "where the system establishes whether the person is who she (implicitly or explicitly) denies to be". The latter function can only be achieved through biometrics since other methods of personal recognition, such as passwords, PINs, or keys, are ineffective. The first time an individual uses a biometric system is called enrollment. During enrollment, biometric information from an individual is captured and stored. In subsequent uses, biometric information is detected and compared with the information stored at the time of enrollment. Note that it is crucial that storage and retrieval of such systems themselves be secure if the biometric system is to be robust. The first block (sensor) is the interface between the real world and the system; it has to acquire all the necessary data. Most of the time it is an image acquisition system, but it can change according to the characteristics desired. The second block performs all the necessary pre-processing: it has to remove artifacts from the sensor, to enhance the input (e.g. removing background noise), to use some kind of normalization, etc. In the third block, necessary features are extracted. This step is an important step as the correct features need to be extracted in an optimal way. A vector of numbers or an image with particular properties is used to create a template. A template is a synthesis of the relevant characteristics extracted from the source. Elements of the biometric measurement that are not used in the comparison algorithm are discarded in the template to reduce the file size and to protect the identity of the enrollee. However, depending on the scope of the biometric system, original biometric image sources may be retained, such as the PIV-cards used in the Federal Information Processing Standard Personal Identity Verification (PIV) of Federal Employees and Contractors (FIPS 201). During the enrollment phase, the template is simply stored somewhere (on a card or within a database or both). During the matching phase, the obtained template is passed to a matcher that compares it with other existing templates, estimating the distance between them using any algorithm (e.g. Hamming distance). The matching program will analyze the template with the input. This will then be output for a specified use or purpose (e.g. entrance in a restricted area), though it is a fear that the use of biometric data may face mission creep. Selection of biometrics in any practical application depending upon the characteristic measurements and user requirements. In selecting a particular biometric, factors to consider include, performance, social acceptability, ease of circumvention and/or spoofing, robustness, population coverage, size of equipment needed and identity theft deterrence. The selection of a biometric is based on user requirements and considers sensor and device availability, computational time and reliability, cost, sensor size, and power consumption.

Figure 1: Continuous Authentication using Behavioral Biometrics | Devpost False match rate (FMR, also called FAR = False Accept Rate): the probability that the system incorrectly matches the input pattern to a non-matching template in the database. It measures the percent of invalid inputs that are incorrectly accepted. In case of similarity scale, if the person is an imposter in reality, but the matching score is higher than the threshold, then he is treated as genuine. This increases the FMR, which thus also depends upon the threshold value. False non-match rate (FNMR, also called FRR = False Reject Rate): the probability that the system fails to detect a match between the input pattern and a matching template in the database. It measures the percent of valid inputs that are incorrectly rejected. Receiver operating characteristic or relative operating characteristic (ROC): The ROC plot is a visual characterization of the trade-off between the FMR and the FNMR. In general, the matching algorithm performs a decision based on a threshold that determines how close to a template the input needs to be for it to be considered a match. If the threshold is reduced, there will be fewer false non-matches but more false accepts. Conversely, a higher threshold will reduce the FMR but increase the FNMR. A common variation is the Detection error trade-off (DET), which is obtained using normal deviation scales on both axes. This more linear graph illuminates the differences for higher performances (rarer errors). Equal error rate or crossover error rate (EER or CER): the rate at which both acceptance and rejection errors are equal. The value of the EER can be easily obtained from the ROC curve. The EER is a quick way to compare the accuracy of devices with different ROC curves. In general, the device with the lowest EER is the most accurate. Failure to enroll rate (FTE or FER): the rate at which attempts to create a template from an input is unsuccessful. This is most commonly caused by low-quality inputs. Failure to capture rate (FTC): Within automatic systems, the probability that the system fails to detect a biometric input when presented correctly.

Template capacity: the maximum number of sets of data that can be stored in the system. An early cataloguing of fingerprints dates back to 1885 when Juan Vucetich started a collection of fingerprints of criminals in Argentina. Josh Ellenbogen and Nitzan Lebovic argued that Biometrics originated in the identification systems of criminal activity developed by Alphonse Bertillon (1853–1914) and by Francis Galton's theory of fingerprints and physiognomy.

Historian Keith Breckenridge note that Galton's travels in South Africa reinforced his early racial prejudices and inspired his later commitment to classifying human difference scientifically. According to Lebovic, Galton's work "led to the application of mathematical models to fingerprints, phrenology, and facial characteristics", as part of "absolute identification" and "a key to both inclusion and exclusion" of populations. Accordingly, "the biometric system is the absolute political weapon of our era" and a form of "soft control". The theoretician David Lyon showed that during the past two decades biometric systems have penetrated the civilian market, and blurred the lines between governmental forms of control and private corporate control.

Kelly A. Gates identified 9/11 as the turning point for the cultural language of our present: "in the language of cultural studies, the aftermath of 9/11 was a moment of articulation, where objects or events that have no necessary connection come together and a new discourse formation is established: automated facial recognition as a homeland security technology." It is possible that data obtained during biometric enrollment may be used in ways for which the enrolled individual has not consented. For example, most biometric features could disclose physiological and/or pathological medical conditions (e.g., some fingerprint patterns are related to chromosomal diseases, iris patterns could reveal sex, hand vein patterns could reveal vascular diseases, most behavioral biometrics could reveal neurological diseases, etc.). Moreover, second generation biometrics, notably behavioral and electro-physiologic biometrics (e.g., based on electrocardiography, electroencephalography, electromyography), could be also used for emotion detection. There are three categories of privacy concerns: Unintended functional scope: The authentication goes further than authentication, such as finding a tumor. Unintended application scope: The authentication process correctly identifies the subject when the subject did not wish to be identified.

Covert identification: The subject is identified without seeking identification or authentication, i.e. a subject's face is identified in a crowd. In terms of recognition or identification performance for a given trait, a biometric system should not exhibit accuracy differences across demographic groups. Disparities in accuracy can lead to uneven error rates for populations defined by gender, race, age, or other attributes. Therefore, biometric systems should be designed and evaluated to ensure demographic fairness, providing equitable performance for all users.

Figure 2: Continuous Authentication: Moving Beyond Logins with Behavioral ... To ensure we can shut down terrorist networks before they ever get to the United States, we must also take the lead in driving international biometric standards. By developing compatible systems, we will be able to securely share terrorist information internationally to bolster our defenses. Just as we are improving the way we collaborate within the U.S. Government to identify and weed out terrorists and other dangerous people, we have the same obligation to work with our partners abroad to prevent terrorists from making any move undetected. Biometrics provide a new way to bring terrorists' true identities to light, stripping them of their greatest advantage—remaining unknown. According to an article written in 2009 by S. Magnuson in the National Defense Magazine entitled "Defense Department Under Pressure to Share Biometric Data" the United States has bilateral agreements with other nations aimed at sharing biometric data. To quote that article: Miller [a consultant to the Office of Homeland Defense and America's security affairs] said the United States has bilateral agreements to share biometric data with about 25 countries. Every time a foreign leader has visited Washington during the last few years, the State Department has made sure they sign such an agreement.

GDPR in the European Union became law in 2018 LGPD in Brazil became law in 2020 Protection of Personal Information Act in South Africa came into force in 2020 Personal Data Protection Act in Sri Lanka implementation started in 2023 Countries using biometrics include Australia, Brazil, Bulgaria, Canada, Cyprus, Greece, China, Gambia, Germany, India, Iraq, Ireland, Israel, Italy, Malaysia, Netherlands, New Zealand, Nigeria, Norway, Pakistan, Poland, South Africa, Saudi Arabia, Tanzania, Turkey, Ukraine, United Arab Emirates, United Kingdom, United States and Venezuela. Among low to middle income countries, roughly 1.2 billion people have already received identification through a biometric identification program.

There are also numerous countries applying biometrics for voter registration and similar electoral purposes. According to the International IDEA's ICTs in Elections Database, some of the countries using (2017) Biometric Voter Registration (BVR) are Armenia, Angola, Bangladesh, Bhutan, Bolivia, Brazil, Burkina Faso, Cambodia, Cameroon, Chad, Colombia, Comoros, Congo (Democratic Republic of), Costa Rica, Ivory Coast, Dominican Republic, Fiji, Gambia, Ghana, Guatemala, India, Iraq, Kenya, Lesotho, Liberia, Malawi, Mali, Mauritania, Mexico, Morocco, Mozambique, Namibia, Nepal, Nicaragua, Nigeria, Panama, Peru, Philippines, Senegal, Sierra Leone, Solomon Islands, Somaliland, Swaziland, Tanzania, Uganda, Uruguay, Venezuela, Yemen, Zambia, and Zimbabwe.

Figure 3: Behavioral Biometric Authentication – KYQO Authentication (from Greek: αIθεντικIς authentikos, "real, genuine", from αIθIντης authentes, "author") is the act of proving an assertion, such as the identity of a computer system user. In contrast with identification, the act of indicating a person or thing's identity, authentication is the process of verifying that identity. Authentication is relevant to multiple fields. In art, antiques, and anthropology, a common problem is verifying that a given artifact was produced by a certain person, or in a certain place (i.e. to assert that it is not counterfeit), or in a given period of history (e.g. by determining the age via carbon dating). In computer science, verifying a user's identity is often required to allow access to confidential data or systems. It might involve validating personal identity documents.

Authentication can be considered to be of three types: The first type of authentication is accepting proof of identity given by a credible person who has first-hand evidence that the identity is genuine. When authentication is required for art or physical objects, this proof could be a friend, family member, or colleague attesting to the item's provenance, perhaps by having witnessed the item in its creator's possession. With autographed sports memorabilia, this could involve someone attesting that they witnessed the object being signed. A vendor selling branded items implies authenticity, while they may not have evidence that every step in the supply chain was authenticated. The second type of authentication is comparing the attributes of the object itself to what is known about objects of that origin. For example, an art expert might look for similarities in the style of painting, check the location and form of a signature, or compare the object to an old photograph. An archaeologist, on the other hand, might use carbon dating to verify the age of an artifact, do a chemical and spectroscopic analysis of the materials used, or compare the style of construction or decoration to other artifacts of similar origin. The physics of sound and light, and comparison with a known physical environment, can be used to examine the authenticity of audio recordings, photographs, or videos. Documents can be verified as being created on ink or paper readily available at the time of the item's implied creation.

Attribute comparison may be vulnerable to forgery. In general, it relies on the facts that creating a forgery indistinguishable from a genuine artifact requires expert knowledge, that mistakes are easily made, and that the amount of effort required to do so is considerably greater than the amount of profit that can be gained from the forgery. In art and antiques, certificates are of great importance for authenticating an object of interest and value. Certificates can, however, also be forged, and the authentication of these poses a problem. For instance, the son of Han van Meegeren, the well-known art-forger, forged the work of his father and provided a certificate for its provenance as well. Criminal and civil penalties for fraud, forgery, and counterfeiting can reduce the incentive for falsification, depending on the risk of getting caught. Currency and other financial instruments commonly use this second type of authentication method. Bills, coins, and cheques incorporate hard-to-duplicate physical features, such as fine printing or engraving, distinctive feel, watermarks, and holographic imagery, which are easy for trained receivers to verify. The third type of authentication relies on documentation or other external affirmations. In criminal courts, the rules of evidence often require establishing the chain of custody of evidence presented. This can be accomplished through a written evidence log, or by testimony from the police detectives and forensics staff that handled it. Some antiques are accompanied by certificates attesting to their authenticity. Signed sports memorabilia is usually accompanied by a certificate of authenticity. These external records have their own problems of forgery and perjury and are also vulnerable to being separated from the artifact and lost.

Taggant fingerprinting – uniquely coded microscopic materials that are verified from a database Encrypted micro-particles – unpredictably placed markings (numbers, layers and colors) not visible to the human eye Holograms – graphics printed on seals, patches, foils or labels and used at the point of sale for visual verification Micro-printing – second-line authentication often used on currencies Serialized barcodes UV printing – marks only visible under UV light Track and trace systems – use codes to link products to the database tracking system Water indicators – become visible when contacted with water DNA tracking – genes embedded onto labels that can be traced Color-shifting ink or film – visible marks that switch colors or texture when tilted Tamper evident seals and tapes – destructible or graphically verifiable at point of sale 2d barcodes – data codes that can be tracked RFID chips NFC chips A difficult-to-reproduce physical artifact, such as a seal, signature, watermark, special stationery, or fingerprint. A shared secret, such as a passphrase, in the content of the message. An electronic signature; public-key infrastructure is often used to cryptographically guarantee that a message has been signed by the holder of a particular private key. The opposite problem is the detection of plagiarism, where information from a different author is passed off as a person's own work. A common technique for proving plagiarism is the discovery of another copy of the same or very similar text, which has different attribution. In some cases, excessively high quality or a style mismatch may raise suspicion of plagiarism.

The ways in which someone may be authenticated fall into three categories, based on what is known as the factors of authentication: something the user knows, something the user has, and something the user is. Each authentication factor covers a range of elements used to authenticate or verify a person's identity before being granted access, approving a transaction request, signing a document or other work product, granting authority to others, and establishing a chain of authority. Security research has determined that for a positive authentication, elements from at least two, and preferably all three, factors should be verified. The three factors (classes) and some of the elements of each factor are: Knowledge: Something the user knows (e.g., a password, partial password, passphrase, personal identification number (PIN), challenge–response (the user must answer a question or pattern), security question). Ownership: Something the user has (e.g., wrist band, ID card, security token, implanted device, cell phone with a built-in hardware token, software token, or cell phone holding a software token). Inherence: Something the user is or does (e.g., fingerprint, retinal pattern, DNA sequence (there are assorted definitions of what is sufficient), signature, face, voice, unique bio-electric signals, or other biometric identifiers). Historically, fingerprints have been used as the most authoritative method of authentication, but court cases in the US and elsewhere have raised fundamental doubts about fingerprint reliability. Outside of the legal system as well, fingerprints are easily spoofable, with British Telecom's top computer security official noting that "few" fingerprint readers have not already been tricked by one spoof or another. Hybrid or two-tiered authentication methods offer a compelling solution, such as private keys encrypted by fingerprint inside of a USB device.

Multi-factor authentication involves two or more authentication factors (something you know, something you have, or something you are). Two-factor authentication is a special case of multi-factor authentication involving exactly two factors. For example, using a bank card (something the user has) along with a PIN (something the user knows) provides two-factor authentication. Business networks may require users to provide a password (knowledge factor) and a pseudorandom number from a security token (ownership factor). Access to a very-high-security system might require a mantrap screening of height, weight, facial, and fingerprint checks (several inherence factor elements) plus a PIN and a day code (knowledge factor elements), but this is still a two-factor authentication.

Enrollment – an individual applies to a credential service provider (CSP) to initiate the enrollment process. After successfully proving the applicant's identity, the CSP allows the applicant to become a subscriber. Authentication – After becoming a subscriber, the user receives an authenticator e.g., a token and credentials, such as a user name. He or she is then permitted to perform online transactions within an authenticated session with a relying party, where they must provide proof that he or she possesses one or more authenticators. Life-cycle maintenance – the CSP is charged with the task of maintaining the user's credential over the course of its lifetime, while the subscriber is responsible for maintaining his or her authenticator(s).

The authentication of information can pose special problems with electronic communication, such as vulnerability to man-in-the-middle attacks, whereby a third party taps into the communication stream, and poses as each of the two other communicating parties, in order to intercept information from each. Extra identity factors can be required to authenticate each party's identity.

Multi-factor authentication (MFA), also known as two-factor authentication (2FA), is an electronic authentication method in which a user is granted access to a website or application only after successfully presenting two or more distinct types of evidence (or factors) to an authentication mechanism. MFA protects personal data—which may include personal identification or financial assets—from being accessed by an unauthorized third party that may have been able to discover, for example, a single password. Usage of MFA has increased in recent years. Security issues which can cause the bypass of MFA are fatigue attacks, phishing and SIM swapping. Accounts with MFA enabled are significantly less likely to be compromised.

Something the user has: Any physical object in the possession of the user, such as a security token (possibly shaped like a USB stick), a bank card, a key, a phone that can be reached at a certain number, etc. Something the user knows: Certain knowledge only known to the user, such as a password, PIN, PUK, etc. Something the user is: Some physical characteristic of the user (biometrics), such as a fingerprint, eye iris, voice, typing speed, pattern in key press intervals, etc. An example of two-factor authentication is the withdrawing of money from an ATM; only the correct combination of a physically present bank card (something the user possesses) and a PIN (something the user knows) allows the transaction to be carried out. Two other examples are to supplement a user-controlled password with a one-time password (OTP) or code generated or received by an authenticator (e.g. a security token or smartphone) that only the user possesses. An authenticator app enables two-factor authentication in a different way, by showing a randomly generated and constantly refreshing code, rather than sending an SMS or using another method. This code is a Time-based one-time password (a TOTP), and the authenticator app contains the key material that allows the generation of these codes.

Knowledge factors ("something only the user knows") are a form of authentication. In this form, the user is required to prove knowledge of a secret in order to authenticate. A password is a secret word or string of characters that is used for user authentication. This is the most commonly used mechanism of authentication. Many multi-factor authentication techniques rely on passwords as one factor of authentication. Variations include both longer ones formed from multiple words (a passphrase) and the shorter, purely numeric, PIN commonly used for ATM access. Traditionally, passwords are expected to be memorized, but can also be written down on a hidden paper or text file.

Possession factors ("something only the user has") have been used for authentication for centuries, in the form of a key to a lock. The basic principle is that the key embodies a secret that is shared between the lock and the key, and the same principle underlies possession factor authentication in computer systems. A security token is an example of a possession factor. Disconnected tokens have no connections to the client computer. They typically use a built-in screen to display the generated authentication data, which is manually typed in by the user. This type of token mostly uses a one-time password that can only be used for that specific session.

Connected tokens are devices that are physically connected to the computer to be used. Those devices transmit data automatically. There are a number of different types, including USB tokens, smart cards and wireless tags. Increasingly, FIDO2 capable tokens, supported by the FIDO Alliance and the World Wide Web Consortium (W3C), have become popular, with mainstream browser support beginning in 2015. A software token (a.k.a. soft token) is a type of two-factor authentication security device that may be used to authorize the use of computer services. Software tokens are stored on a general-purpose electronic device such as a desktop computer, laptop, PDA, or mobile phone and can be duplicated – contrast hardware tokens, where the credentials are stored on a dedicated hardware device and therefore cannot be duplicated, absent physical invasion of the device. A soft token may not be a device the user interacts with. Typically an X.509v3 certificate is loaded onto the device and stored securely to serve this purpose. Multi-factor authentication can also be applied in physical security systems, where it is typically deployed through the use, firstly, of a physical possession (such as a fob, keycard, or QR-code displayed on a device) which acts as the identification credential, and secondly, a validation of one's identity such as facial biometrics or retinal scan (facial verification or facial authentication), or a PIN.

No additional tokens are necessary because it uses mobile devices that are (usually) carried all the time. As they are constantly changed, dynamically generated passcodes are safer to use than fixed (static) log-in information. Depending on the solution, passcodes that have been used are automatically replaced in order to ensure that a valid code is always available, transmission/reception problems do not, therefore, prevent logins. Disadvantages: Users may still be susceptible to phishing attacks. An attacker can send a text message that links to a spoofed website that looks identical to the actual website - a Man-in-the-middle attack. The attacker can then get the authentication code, user name and password. A mobile phone is not always available—it can be lost, stolen, have a dead battery, or otherwise not work. In particular, lost devices unrelated to a security incident is a major weakness of mass-adoption (in comparison with a lost device and not using MFA.) Answers to solve this weakness often introduce further complexities to MFA adoption. Despite their growing popularity, some users may not even own a mobile device, and take umbrage at being required to own one as a condition of using some service on their home PC. The SS7 system underlying mobile phone roaming has vulnerabilities, and SMS messages may be redirected to attackers. Mobile phone reception is not always available—large areas, particularly outside of towns, lack coverage. SIM cloning gives hackers access to mobile phone connections. SIM swap attacks and Social-engineering attacks against mobile-operator companies have resulted in the handing over of duplicate SIM cards to criminals. Text messages to mobile phones using SMS are insecure and can be intercepted by IMSI-catchers. Thus third parties can steal and use the token. Account recovery typically bypasses mobile-phone two-factor authentication. Modern smartphones are used both for receiving email and SMS. So if the phone is lost or stolen and is not protected by a password or biometric, all accounts for which the email is the key can be taken over as the phone can receive the second factor. Mobile carriers may charge the user messaging fees.

In 2013, Kim Dotcom claimed to have invented two-factor authentication in a 2000 patent, and briefly threatened to sue all the major web services. However, the European Patent Office revoked his patent in light of an earlier 1998 U.S. patent held by AT&T. TwoFactorAuth.org - TwoFactorAuth.org - An in-depth online resource for (2FA) and all it entails Attackers breached the servers of RSA and stole information that could be used to compromise the security of two-factor authentication tokens used by 40 million employees (register.com, 18 Mar 2011) Banks to Use Two-factor Authentication by End of 2006, (slashdot.org, 20 Oct 2005) Microsoft to abandon passwords, Microsoft preparing to dump passwords in favour of two-factor authentication in forthcoming versions of Windows (vnunet.com, 14 Mar 2005) A neural network-based authentication system using keystroke dynamics to identify users by their unique typing patterns.

Behavioral biometrics is a security method that verifies user identity by analyzing unique patterns in how people interact with digital devices and systems. Unlike traditional authentication that relies on what you know (passwords) or what you have (tokens), behavioral auth identifies you based on how you naturally type, move your mouse, hold your phone, or navigate through applications. This authentication approach uses machine learning algorithms to build a behavioral profile for each user, then continuously monitors activity to detect anomalies that signal unauthorized access or fraudulent behavior.

The tech operates passively in the background, creating a frictionless security layer that doesn't interrupt legitimate users while flagging suspicious activity in real time. Behavioral biometrics systems capture and analyze thousands of micro-behaviors during user sessions by establishing a baseline behavioral profile through an enrollment phase, then using machine learning models to authenticate users in subsequent sessions by comparing their current behavior against this profile.

2. System Architecture & Core Working Principles The technology monitors both physical behaviors (typing speed, keystroke rhythm, mouse movement velocity, touchscreen pressure, device handling angles) and cognitive behaviors (navigation patterns, task completion sequences, form-filling habits, decision-making timing).

When significant deviations from the established profile are detected, behavioral biometrics solutions trigger security responses ranging from additional verification requests to session termination, depending on risk level and organizational policies. An example of behavioral biometric authentication is typing biometrics, where users are identified and authenticated based on their typing patterns.

"Typing biometrics (or keystroke dynamics) identifies and authenticates based on how you type. When every other passwordless method has been stripped away, your unique typing patterns become the credential." Behavioral biometrics encompasses several distinct methods, each analyzing different interaction patterns. Organizations often deploy multiple types simultaneously to create a comprehensive authentication profile.

Typing biometrics, also known as keystroke dynamics , measures the unique rhythm and pattern of how someone types. The system captures dwell time (how long keys are pressed), flight time (intervals between keystrokes), typing speed variations, and pressure patterns. This method proves particularly valuable in device-restricted environments where other biometric options aren't available.

Mouse behavior analysis tracks movement speed, acceleration patterns, click frequency, cursor trajectory, and scrolling habits. Legitimate users demonstrate consistent mouse patterns, while attackers often exhibit mechanical or erratic movements that deviate from the established baseline. For mobile and tablet devices, touchscreen biometrics examines swipe gestures, tap pressure, finger size on screen, multi-touch patterns, and device orientation during use. These behaviors create a distinct signature that's difficult for unauthorized users to replicate.

This category captures how users physically hold and manipulate their devices. Sensors measure device angle, grip pressure, hand tremor patterns, and movement while typing or reading. These unconscious behaviors provide an additional authentication layer that operates continuously. Beyond individual actions, behavioral authentication systems analyze broader session patterns including login times, typical session duration, application usage sequences, transaction patterns, and navigation flows through systems. Unusual patterns in these areas often indicate account compromise or social engineering attacks in progress.

Traditional biometric authentication relies on static physical characteristics like fingerprints, facial features, iris patterns, or vein structure. These methods require specific hardware (fingerprint readers, cameras) and provide authentication only at the point of entry. No specialized hardware required - Works with standard keyboards, mice, and touchscreens Continuous authentication - Monitors throughout the session rather than a single checkpoint Can't be lost or stolen - Users can't lose behavioral patterns like they might lose a token Complements physical biometrics - Physical biometrics provide strong initial authentication while behavioral biometrics maintain security throughout the session A high volume of password resets is already a challenge for enterprises that allow for common methods of authentication.

This pain only deepens in secure work environments where smartphones are prohibited, cameras are banned, and hardware tokens at scale are cost-prohibitive. Workers in these environments default to static passwords âII often 18 to 24 characters âII that rotate every two months. This constant rotation leads to widespread credential sharing and productivity loss at every shift change. Organizations need a passwordless solution that functions without any additional equipment or devices.

Typing biometrics is a behavioral authentication method that identifies you based on how you type. When every other passwordless method has been stripped away, your unique typing patterns become the credential. The technology uses machine learning to build a keystroke dynamics profile for each user based on their natural typing rhythm, pressure, and cadence. This profile becomes a biometric factor that, when paired with a PIN, enables two-step passwordless verification from a keyboard alone.

It's also one of the only solutions delivering passwordless authentication on shared workstations without additional equipment. The authentication flow is longer than a fingerprint tap or facial scan, which is by design, since keystroke dynamics is positioned for environments where every other option has been ruled out. Behavioral authentication via typing biometrics represents the last mile to passwordless for high-security, device-free work environments that have ruled out every other factor. The technology proves ideal for several workforce types: BPO and customer experience operations where phones and cameras are prohibited on the floor. Call center environments often ban personal devices to protect customer data and maintain security compliance.

Pharmaceutical and life sciences facilities where gloves and masks block fingerprint and facial recognition. Workers in clean rooms and manufacturing environments need authentication that works with required PPE. Financial services operations using shared workstations in restricted processing environments. Back-office operations and secure processing centers often prohibit devices while requiring strong authentication.

Government and defense facilities with classified spaces and strict device policies. Secure facilities enforce device restrictions that eliminate traditional passwordless options. Concentrix operates across restricted call center environments where end customers prohibit cameras inside secure facilities. Deploying hardware tokens across 450K agents was cost-prohibitive, creating a significant authentication challenge.

The company is rolling out behavioral authentication across its general agent population, with 1Key hardware biometrics retained for agents handling BFSI clients where a higher security threshold is required. The deployment shows how organizations can match authentication methods to specific workforce segments based on 1. Security requirements and 2. Environmental constraints .

Most organizations don't need behavioral authentication as their primary passwordless solution. Fingerprint readers, facial recognition, mobile authenticators, and hardware tokens serve the majority of enterprise use cases more efficiently. However, it can be the only viable option in specific scenarios where security policies or operational constraints eliminate every other method. For example, when: For other workforce segments, faster authentication methods typically provide better user experience while maintaining strong security. The goal is offering choice across diverse environments, rather than forcing a single approach.

If your org operates in environments where smartphones, cameras, and hardware tokens aren't viable, behavioral authentication provides the passwordless solution you need. 1Kosmos offers keystroke dynamics authentication that works on standard Windows workstations without any additional equipment, alongside our hardware element, 1Key.

Download our datasheet on behavioral authentication for restricted environments to learn how keystroke dynamics enables passwordless access when every other option has been ruled out, or contact us if you have any urgent questions about our behavioral auth and passwordless solutions . 3. Software Implementation, Frameworks & Protocols Behavioral biometrics is a security method that verifies user identity by analyzing unique patterns in how people interact with digital devices and systems.

Unlike traditional authentication that relies on what you know (passwords) or what you have (tokens), behavioral auth identifies you based on how you naturally type, move your mouse, hold your phone, or navigate through applications. This authentication approach uses machine learning algorithms to build a behavioral profile for each user, then continuously monitors activity to detect anomalies that signal unauthorized access or fraudulent behavior.

The tech operates passively in the background, creating a frictionless security layer that doesn't interrupt legitimate users while flagging suspicious activity in real time. Behavioral biometrics systems capture and analyze thousands of micro-behaviors during user sessions by establishing a baseline behavioral profile through an enrollment phase, then using machine learning models to authenticate users in subsequent sessions by comparing their current behavior against this profile.

The technology monitors both physical behaviors (typing speed, keystroke rhythm, mouse movement velocity, touchscreen pressure, device handling angles) and cognitive behaviors (navigation patterns, task completion sequences, form-filling habits, decision-making timing). When significant deviations from the established profile are detected, behavioral biometrics solutions trigger security responses ranging from additional verification requests to session termination, depending on risk level and organizational policies.

An example of behavioral biometric authentication is typing biometrics, where users are identified and authenticated based on their typing patterns. "Typing biometrics (or keystroke dynamics) identifies and authenticates based on how you type. When every other passwordless method has been stripped away, your unique typing patterns become the credential." Behavioral biometrics encompasses several distinct methods, each analyzing different interaction patterns. Organizations often deploy multiple types simultaneously to create a comprehensive authentication profile.