Behavioral Biometrics

2010 ◽  
pp. 101-120 ◽  
Author(s):  
Kenneth Revett
Keyword(s):  
User Authentication ◽  
Error Rates ◽  
Keystroke Dynamics ◽  
Behavioral Biometrics ◽  
Authentication Mechanism ◽  
Computer Based ◽  
Authentication System ◽  
Novel Approaches ◽  
New Form ◽  
Minimal Effort

Behavioral biometrics is a relatively new form of authentication mechanism which relies on the way a person interacts with an authentication device. Traditional instances of this approach include voice, signature, and keystroke dynamics. Novel approaches to behavioral biometrics include biosignals, such as the electroencephalogram and the electrocardiogram. The biosignal approach to user authentication has been shown to produce equal error rates on par with more traditional behavioral biometric approaches. In addition, through a process similar to biofeedback, users can be trained with minimal effort to produce computer-based input via the manipulations of endogenous biosignal patterns. This chapter discusses the use of biosignal based biometrics, highlighting key studies and how this approach can be integrated into a multibiometric user authentication system.

scholarly journals Sequence Alignment with Dynamic Divisor Generation for Keystroke Dynamics Based User Authentication

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Jiacang Ho ◽  
Dae-Ki Kang
Keyword(s):  
Sequence Alignment ◽  
Premature Termination ◽  
User Authentication ◽  
Time Difference ◽  
Experimental Results ◽  
Equal Length ◽  
Keystroke Dynamics ◽  
Major Drawback ◽  
Sequence Generation ◽  
Authentication Mechanism

Keystroke dynamics based authentication is one of the prevention mechanisms used to protect one’s account from criminals’ illegal access. In this authentication mechanism, keystroke dynamics are used to capture patterns in a user typing behavior. Sequence alignment is shown to be one of effective algorithms for keystroke dynamics based authentication, by comparing the sequences of keystroke data to detect imposter’s anomalous sequences. In previous research, static divisor has been used for sequence generation from the keystroke data, which is a number used to divide a time difference of keystroke data into an equal-length subinterval. After the division, the subintervals are mapped to alphabet letters to form sequences. One major drawback of this static divisor is that the amount of data for this subinterval generation is often insufficient, which leads to premature termination of subinterval generation and consequently causes inaccurate sequence alignment. To alleviate this problem, we introduce sequence alignment of dynamic divisor (SADD) in this paper. In SADD, we use mean of Horner’s rule technique to generate dynamic divisors and apply them to produce the subintervals with different length. The comparative experimental results with SADD and other existing algorithms indicate that SADD is usually comparable to and often outperforms other existing algorithms.

scholarly journals Keystroke Dynamics-Based Authentication Using Unique Keypad

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2242
Author(s):  
Maro Choi ◽  
Shincheol Lee ◽  
Minjae Jo ◽  
Ji Sun Shin
Keyword(s):  
User Authentication ◽  
Classification Performance ◽  
Performance Outcomes ◽  
Error Rates ◽  
Personal Identification ◽  
Biometric Authentication ◽  
Keystroke Dynamics ◽  
User Classification ◽  
Wide Range ◽  
Filtering Rates

Authentication methods using personal identification number (PIN) and unlock patterns are widely used in smartphone user authentication. However, these authentication methods are vulnerable to shoulder-surfing attacks, and PIN authentication, in particular, is poor in terms of security because PINs are short in length with just four to six digits. A wide range of research is currently underway to examine various biometric authentication methods, for example, using the user’s face, fingerprint, or iris information. However, such authentication methods provide PIN-based authentication as a type of backup authentication to prepare for when the maximum set number of authentication failures is exceeded during the authentication process such that the security of biometric authentication equates to the security of PIN-based authentication. In order to overcome this limitation, research has been conducted on keystroke dynamics-based authentication, where users are classified by analyzing their typing patterns while they are entering their PIN. As a result, a wide range of methods for improving the ability to distinguish the normal user from abnormal ones have been proposed, using the typing patterns captured during the user’s PIN input. In this paper, we propose unique keypads that are assigned to and used by only normal users of smartphones to improve the user classification performance capabilities of existing keypads. The proposed keypads are formed by randomly generated numbers based on the Mersenne Twister algorithm. In an attempt to demonstrate the superior classification performance of the proposed unique keypad compared to existing keypads, all tests except for the keypad type were conducted under the same conditions in earlier work, including collection-related features and feature selection methods. Our experimental results show that when the filtering rates are 10%, 20%, 30%, 40%, and 50%, the corresponding equal error rates (EERs) for the proposed keypads are improved by 4.15%, 3.11%, 2.77%, 3.37% and 3.53% on average compared to the classification performance outcomes in earlier work.

Fine-Grained and Context-Aware Behavioral Biometrics for Pattern Lock on Smartphones

2021 ◽  
Vol 5 (1) ◽  
pp. 1-30
Author(s):  
Dai Shi ◽  
Dan Tao ◽  
Jiangtao Wang ◽  
Muyan Yao ◽  
Zhibo Wang ◽  
...  
Keyword(s):  
State Of The Art ◽  
Context Aware ◽  
Behavioral Biometrics ◽  
Body Postures ◽  
Fine Grained ◽  
Authentication Mechanism ◽  
System A ◽  
Authentication System

Pattern lock has been widely used in smartphones as a simple and effective authentication mechanism, which however is shown to be vulnerable to various attacks. In this paper, we design a novel authentication system for more secure pattern unlocking on smartphones. The basic idea is to utilize various behavior information of the user during pattern unlocking as additional authentication fingerprints, so that even if the pattern password is leaked to an attacker, the system remains safe and protected. To accommodate a variety of user contexts by our system, a context-aware module is proposed to distinguish any of such contexts (e.g., body postures when drawing the pattern) and use it to guide the authentication. Moreover, we design a polyline weighted strategy with overlapping based on the consistency of pattern lock, which analyzes the behavior information of the user during the unlock process in a fine-grained manner and takes an overall consideration the results of different polylines. Based on 14,850 samples collected from 77 participants, we have extensively evaluated the proposed system. The results demonstrate that it outperforms state-of-the-art implicit authentication based pattern lock approaches, and that each key module in our system is effective.

scholarly journals On the Design and Analysis of a Biometric Authentication System Using Keystroke Dynamics

Cryptography ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 12
Author(s):  
Robert Cockell ◽  
Basel Halak
Keyword(s):  
User Authentication ◽  
Training Data ◽  
Average Error ◽  
Biometric Authentication ◽  
Keystroke Dynamics ◽  
Systematic Analysis ◽  
Average Error Rate ◽  
Multifactor Authentication ◽  
Authentication System ◽  
Cost Efficient

This paper proposes a portable hardware token for user’s authentication; it is based on the use of keystroke dynamics to verify users biometrically. The proposed approach allows for a multifactor authentication scheme, in which a user cannot be granted access unless they provide a correct password on a hardware token and their biometric signature. The latter is extracted while the user is typing their password. This paper explains the design rationale of the proposed system and provides a comprehensive insight in the development of a hardware prototype of the same. The paper also presents a feasibility study that included a systematic analysis based on training data obtained from 32 users. Our results show that dynamic keystroke can be employed to construct a cost-efficient solution for biometric user authentication with an average error rate of 4.5%.

scholarly journals A User Authentication Scheme Using Physiological and Behavioral Biometrics for Multitouch Devices

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Chorng-Shiuh Koong ◽  
Tzu-I Yang ◽  
Chien-Chao Tseng
Keyword(s):  
User Authentication ◽  
Mobile Network ◽  
Authentication Scheme ◽  
Security Level ◽  
Behavioral Biometrics ◽  
Vending Machines ◽  
Privacy Leakage ◽  
Authentication Mechanism ◽  
User Authentication Scheme ◽  
Biometric Information

With the rapid growth of mobile network, tablets and smart phones have become sorts of keys to access personal secured services in our daily life. People use these devices to manage personal finances, shop on the Internet, and even pay at vending machines. Besides, it also helps us get connected with friends and business partners through social network applications, which were widely used as personal identifications in both real and virtual societies. However, these devices use inherently weak authentication mechanism, based upon passwords and PINs that is not changed all the time. Although forcing users to change password periodically can enhance the security level, it may also be considered annoyances for users. Biometric technologies are straightforward because of the simple authentication process. However, most of the traditional biometrics methodologies require diverse equipment to acquire biometric information, which may be expensive and not portable. This paper proposes a multibiometric user authentication scheme with both physiological and behavioral biometrics. Only simple rotations with fingers on multitouch devices are required to enhance the security level without annoyances for users. In addition, the user credential is replaceable to prevent from the privacy leakage.

COMPUTER-BASED LOCAL AREA AUTHENTICATION SYSTEM

2018 ◽  
Vol 6 (3) ◽  
pp. 1
Author(s):  
OMOROGIUWA O. S. ◽  
AZIKEN G. O. ◽  
◽  
Keyword(s):  
Local Area ◽  
Computer Based ◽  
Authentication System
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