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Today, digitalization decisively penetrates all the sides of the modern society. One of the key enablers to maintain this process secure is authentication. It covers many different areas of a hyper-connected world, including online payments, communications, access right management, etc. This work sheds light on the evolution of authentication systems towards Multi-Factor Authentication (MFA) starting from Single-Factor Authentication (SFA) and through Two-Factor Authentication (2FA). Particularly, MFA is expected to be utilized for human-to-everything interactions by enabling fast, user-friendly, and reliable authentication when accessing a service. This paper surveys the already available and emerging sensors (factor providers) that allow for authenticating a user with the system directly or by involving the cloud. The corresponding challenges from the user as well as the service provider perspective are also reviewed. The MFA system based on reversed Lagrange polynomial within Shamir’s Secret Sharing (SSS) scheme is further proposed to enable more flexible authentication. This solution covers the cases of authenticating the user even if some of the factors are mismatched or absent. Our framework allows for qualifying the missing factors by authenticating the user without disclosing sensitive biometric data to the verification entity. Finally, a vision of the future trends in MFA is discussed.
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Article
Multi-Factor Authentication: A Survey
Aleksandr Ometov 1,*ID , Sergey Bezzateev 2ID , Niko Mäkitalo 3ID , Sergey Andreev 1ID ,
Tommi Mikkonen 3ID and Yevgeni Koucheryavy 1ID
1Laboratory of Electronics and Communications Engineering, Tampere University of Technology,
FI-33720 Tampere, Finland; sergey.andreev@tut.fi (S.A.); evgeni.kucheryavy@tut.fi (Y.K.)
2Department of Security of Cyberphysical Systems, ITMO University, St. Petersburg RU-197101, Russia;
bsv@aanet.ru
3Department of Computer Science, University of Helsinki, FI-00014 Helsinki, Finland;
niko.makitalo@helsinki.fi (N.M.); tommi.mikkonen@helsinki.fi (T.M.)
*Correspondence: aleksandr.ometov@tut.fi
This manuscript is an extended version of work by A. Ometov and S. Bezzateev titled “Multi-factor
Authentication: A Survey and Challenges in V2X Applications” presented at the 9 th International Congress
on Ultra Modern Telecommunications and Control Systems (ICUMT) on 6 November 2017.
Received: 30 November 2017; Accepted: 18 December 2017; Published: 5 January 2018
Abstract:
Today, digitalization decisively penetrates all the sides of the modern society. One of
the key enablers to maintain this process secure is authentication. It covers many different
areas of a hyper-connected world, including online payments, communications, access right
management, etc. This work sheds light on the evolution of authentication systems towards
Multi-Factor Authentication (MFA) starting from Single-Factor Authentication (SFA) and through
Two-Factor Authentication (2FA). Particularly, MFA is expected to be utilized for human-to-everything
interactions by enabling fast, user-friendly, and reliable authentication when accessing a service.
This paper surveys the already available and emerging sensors (factor providers) that allow for
authenticating a user with the system directly or by involving the cloud. The corresponding challenges
from the user as well as the service provider perspective are also reviewed. The MFA system based
on reversed Lagrange polynomial within Shamir’s Secret Sharing (SSS) scheme is further proposed to
enable more flexible authentication. This solution covers the cases of authenticating the user even
if some of the factors are mismatched or absent. Our framework allows for qualifying the missing
factors by authenticating the user without disclosing sensitive biometric data to the verification entity.
Finally, a vision of the future trends in MFA is discussed.
Keywords: survey; authentication; SFA; 2FA; MFA; evolution; vision
1. Introduction
The continuous growth in the numbers of smart devices and related connectivity loads has
impacted mobile services seamlessly offered anywhere around the globe [
1
]. In such connected world,
the enabler keeping the transmitted data secure is, in the first place, authentication [24].
According to the fundamental work in [
5
], authentication is a process where a “user identifies
himself by sending
x
to the system; the system authenticates his identity by computing
F(x)
and
checking that it equals the stored value
y
”. This definition has not changed significantly over time
despite the fact that a simple password is no longer the only factor for validating the user from the
information technology perspective [6].
Authentication remains a fundamental safeguard against illegitimate access to the device or any
other sensitive application, whether offline or online [
7
9
] (see Figure 1). Back in time, the transactions
were authenticated primarily by physical presence, i.e., for example, by applying the wax seal [
10
].
Cryptography 2018,2, 1; doi:10.3390/cryptography2010001 www.mdpi.com/journal/cryptography
Cryptography 2018,2, 1 2 of 31
Closer to present days and with the advancement of our civilization, it was realized that the validation
based on the sender identification only is not always adequate on the global scale [11].
******
Login
******
Password
Ownership
Access card with photo
Knowledge
User name and password
Biometrics
Iris recognition
Figure 1. Conceptual authentication examples.
Initially, only one factor was utilized to authenticate the subject. By that time, Single-Factor
Authentication (SFA) was mostly adopted by the community due to its simplicity and user
friendliness [
12
,
13
]. As an example, the use of a password (or a PIN) to confirm the ownership
of the user ID could be considered. Apparently, this is the weakest level of authentication [
14
,
15
].
By sharing the password, one can compromise the account immediately. Moreover, an unauthorized
user can also attempt to gain access by utilizing the dictionary attack [
16
], rainbow table [
17
], or social
engineering techniques [
18
]. Commonly, the minimum password complexity requirement is to be
considered while utilizing this type of authentication [19].
Further, it was realized that authentication with just a single factor is not reliable to provide
adequate protection due to a number of security threats [
20
]. As an intuitive step forward, Two-Factor
Authentication (2FA) [
21
23
] was proposed that couples the representative data (username/password
combination) with the factor of personal ownership, such as a smartcard or a phone [24,25].
Today, three types of factor groups are available to connect an individual with the
established credentials [26]:
1. Knowledge factor—something the user knows, such as a password or, simply, a “secret”;
2. Ownership factor—something the user has, such as cards, smartphones, or other tokens;
3. Biometric factor—something the user is, i.e., biometric data or behavior pattern.
Subsequently, Multi-Factor Authentication (MFA) was proposed to provide a higher level of
safety and facilitate continuous protection of computing devices as well as other critical services from
unauthorized access by using more than two categories of credentials [
27
29
]. For the most part, MFA
is based on biometrics, which is automated recognition of individuals based on their behavioral [
30
,
31
]
and biological characteristics [
32
]. This step offered an improved level of security as the users were
required to present the evidence of their identity, which relies on two or more different factors [
33
].
The discussed evolution of authentication methods is shown in Figure 2.
Cryptography 2018,2, 1 3 of 31
Single-factor authencaon
Knowledge factor:
PIN, password,
security quesons
****
Two-factor authencaon
Ownership factor:
Smartphone, key-card,
one-me password
Mul-factor authencaon
Biometric factor:
Fingerprint, face recognion,
behavior recognion
Figure 2. Evolution of authentication methods from SFA to MFA.
Today, MFA is expected to be utilized in scenarios where safety requirements are higher than
usual [
34
,
35
]. According to SC Media UK, 68 percent of Europeans are willing to use biometric
authentication for payments [
36
] Consider the daily routine of ATM cash withdrawal [
37
,
38
]. Here,
the user has to provide a physical token (a card) representing the ownership factor and support
it with a PIN code representing the knowledge factor to be able to access a personal account and
withdraw money.
This system could be easily made more complex by adding the second channel like, for example,
a one-time password to be entered after both the card and the user password were presented [
39
,
40
].
In a more interesting scenario, it could be done with the facial recognition methods [
41
,
42
]. Moreover,
a recent survey discovered that 30 percent of enterprises planned to implement the MFA solution in
2017, with 51 percent claiming that they already utilize MFA, and 38 percent saying that they utilize it
in “some areas” of operation [
43
]. This evidence supports the MFA as an extremely promising direction
of the authentication evolution.
As one of the interesting future trends, authentication between a vehicle and its owner or a
temporary user may be considered. Based on the statistics [
44
], a vehicle is stolen every 45 s in the U.S.
The current authentication method that allows for starting and using the vehicle is still an immobilizer
key [
45
,
46
]. The MFA may significantly improve access to most of the electronic devices from both
security and user experience perspectives [47,48].
Generally, MFA applications could be divided into three market-related groups: (i) commercial
applications [
49
,
50
], i.e., account login, e-commerce, ATM, physical access control, etc.; (ii) governmental
applications [
51
,
52
], i.e., identity documents, government ID, passport, driver’s license, social security,
border control, etc.; and (iii) forensic applications [
53
,
54
], i.e., criminal investigation, missing children,
corpse identification, etc. Generally, the number of scenarios related to authentication is indeed large.
Today, MFA becomes an extremely critical factor for:
Validating the identity of the user and the electronic device (or its system) [55,56];
Validating the infrastructure connection [57];
Validating the interconnected IoT devices, such as a smartphone, tablet, wearable device, or any
other digital token (key dongle) [58].
Presently, one of the main MFA challenges is the absence of correlation between the user identity
and the identities of smart sensors within the electronic device/system [
59
]. Regarding security, this
relationship must be established so that only the legitimate operator, e.g., the one whose identity is
authenticated in advance, can gain the access rights [
60
,
61
]. At the same time, the MFA process should
be as user-friendly as possible, for example:
1.
Customers first register and authenticate with the service provider to activate and manage
services they are willing to access;
2. Once accessing the service, the user is required to pass a simple SFA with the fingerprint/token
signed in advance by the service provider;
3.
Once initially accepted by the system, the customer authenticates by logging in with the
same username and password as setup previously in the customer portal (or social login).
Cryptography 2018,2, 1 4 of 31
For additional security, the managing platform can enable secondary authentication factors.
Once the user has successfully passed all the tests, the framework automatically authenticates to
the service platform;
4.
The secondary authentication occurs automatically based on the biometric MFA, so the user
would be requested to enter an additional code or provide a token password only in case the
MFA fails.
Biometrics indeed significantly contribute to the MFA scheme and can dramatically improve
identity proofing by pairing the knowledge factor with the multimodal biometric factors [
62
,
63
],
thus making it much more difficult for a criminal to eavesdrop on a system while pretending to be
another person. However, the utilization of biological factors has its challenges mainly related to the
ease of use [64], which largely impacts the MFA system usability.
From the user experience perspective, fingerprint scanner already provides the most widely
integrated biometric interface. This is mainly due to its adoption by smartphone vendors on the
market [
65
]. On the other hand, it is not recommended to be utilized as a standalone authentication
method [
66
]. However, the use of any biometrics often requires a set of separate sensing devices.
The utilization of already integrated ones allows for reducing the authentication system costs and
facilitate the adoption by end users. A fundamental trade-off between usability and security is one of
the critical drivers when considering the authentication systems of today [67].
Another challenge is that the use of biometrics relies on a binary decision mechanism [
68
].
This was well studied over past decades in classical statistical decision theory from the authentication
perspective [
69
,
70
]. There are various possible solutions to control a slight mismatch of the actual
“measured” biometrics and the data stored in previously captured samples. The two widely utilized
techniques are: false accept rate (FAR) [
71
] and false reject rate (FRR) [
72
]. Manipulations with the
decision criteria allow adjusting the authentication framework based on the predefined cost, risks, and
benefits. The MFA operation is highly dependent on FAR and FRR, since obtaining zero values for
both of the metrics is almost infeasible. The evaluation of more than one biometric feature to establish
the identity of an individual can improve the operation of the MFA system dramatically [73].
Since the currently available literature faces a lack of detailed MFA analysis suitable for
non-specialists in the field, the main contributions of this work are as follows:
1.
This work provides a detailed analysis of factors that are presently utilized for MFA with their
corresponding operational requirements. Potential sensors to be utilized are surveyed based on
the academic and industrial sources (Section 2);
2.
The survey is followed by the challenges related to MFA adoption from both the user experience
and the technological perspectives (Section 3);
3.
Further, the framework based on the reversed Lagrange polynomial is proposed to allow for
utilizing MFA in cases where some of the factors are missing (Section 4). A discussion on the
potential evaluation methodology is also provided;
4. Finally, the vision of the future of MFA is discussed (Section 5).
2. State-of-the-Art and Potential MFA Sources
Presently, the authentication systems already utilize an enormous number of sensors that enable
identification of a user. In this section, we elaborate on the MFA-suitable factors, corresponding
market-available sensors, and related challenges. Furthermore, we provide additional details on the
ones that are to be potentially deployed in the near future.
2.1. Widely Deployed MFA Sensors/Sources
Today, identification and authentication for accessing sensitive data are one of the primary use
cases for MFA. We further list the factors already available for the MFA utilization without acquiring
additional specialized equipment.
Cryptography 2018,2, 1 5 of 31
2.1.1. Password Protection
The conventional way to authenticate a user is to request a PIN code, password, etc. [
74
]. The secret
pass-phrase traditionally represents a knowledge factor. It requires only a simple input device (at least
one button) to authenticate the user.
2.1.2. Token Presence
The password could then be supplemented with a physical token—for example, a card, which is
recommended as a second factor group—the ownership [
75
,
76
]. From the hardware perspective, a user
may present a smartcard, phone, wearable device, etc., which are more complicated to delegate [
77
].
In this case, the system should be equipped with a radio interface allowing for two-way communication
with the token [
78
,
79
]. On the other hand, the most widely known software token is one-time software
generated password [
80
]. The main drawback of the above is the problem of uncontrollable duplication.
2.1.3. Voice Biometrics
Most of the contemporary smart electronic devices are equipped with a microphone that allows
utilizing voice recognition as a factor for MFA [
81
,
82
]. At the same time, the technology advancement
of tomorrow may allow special agencies not only to recognize the speakers but also to mimic their
voices including the intonation, timbre, etc., which is a serious drawback of utilizing voice as a primary
authentication method [83,84].
2.1.4. Facial Recognition
As the next step, facial recognition could be considered. At the beginning of its development,
the technology was based on the landmark picture analysis, which was relatively simple to replicate
by supplying the system with a photo [
85
]. The next phase was by enabling three-dimensional face
recognition, i.e., by asking the user to move head during the authentication process in a specific
manner [
86
,
87
]. Finally, the advancement of this system reached the point of recognizing the actual
expressions of the user [
88
]. To enable facial recognition, it is required to equip the system with at least
one output device and a camera [89].
2.1.5. Ocular-Based Methodology
The iris recognition techniques are on the market for more than 20 years [
90
]. This approach
does not require the user to be close to the capture device while analyzing the color pattern of the
human eye [
91
]. Retina analysis is another attractive technique [
92
]. Here, a thin tissue composed of
neural cells that are located in the posterior portion of the eye is captured and analyzed. Because of the
complex structure of the capillaries that supply the retina with blood, each person’s retina is unique.
The most prominent challenges in those methods are the need for high quality capture device and
robust mathematical technique to analyze the image [93].
2.1.6. Hand Geometry
Some systems employ the analysis of the physical shape of a hand to authenticate the user.
Initially, pegs were utilized to validate the subject, but the usability of such methods was low [
94
].
Further on, the flatbed scanner was used to obtain the image without the need to fix the user’s hand
in one specific position [
95
]. Today, some systems utilize conventional cameras not requiring close
contact with the capture surface. This approach is, however, not very robust to the environment [
96
].
Some vendors apply so-called photoplethysmography (PPG) to determine whether a wearable device
(e.g., a smartwatch) is currently on its user’s wrist or not [
97
,
98
]. The process is similar to the one
followed when measuring heart rate [99].
Cryptography 2018,2, 1 6 of 31
2.1.7. Vein Recognition
The advances in fingerprint scanners offer an opportunity to collect the vein picture of the
finger as well [
100
]. More complicated devices utilize palm print recognition to acquire and store the
shape/movement of the entire hand [
101
,
102
]. At the current stage of development, vein biometrics
are still vulnerable to spoofing attacks [103,104].
2.1.8. Fingerprint Scanner
Utilizing fingerprint scanner as the primary authentication mechanism is currently being pushed
by the majority of smartphone/personal computer vendors [
105
]. This solution is intuitive to use but
remains extremely simple to fabricate—mainly due to the fact that our fingerprints could be obtained
from almost anything we touch [
106
,
107
]. The integration potential of this method is indeed high [
108
],
even though it is also not recommended to be used as a standalone authentication approach. Most of
the smartphone vendors install an additional camera to obtain the fingerprint instead of more safe
vein recognition.
2.1.9. Thermal Image Recognition
Similarly to vein recognition, thermal sensor is utilized to reconstruct the unique thermal image
of one’s body blood flow in proximity [
109
,
110
]. Many challenges with this authentication method
may arise due to the user conditions: sickness or emotion may significantly influence the perceived
figures [111].
2.1.10. Geographical Location
Utilizing the device’s and user’s geographical location to validate whether access to the
device/service could be granted is a special case of location-based authentication [
112
,
113
].
Importantly, GPS signal could be easily jammed or considered faulty due to the propagation properties;
thus, it is recommended to utilize at least two location sources, for example, GPS and wireless network
cell ID [114]. A smartphone could be used to support MFA from the location acquisition perspective.
2.2. Future of MFA Integration
Accelerated adoption across many industries as well as increased availability of biometric services
in a wide range of readily-available consumer products is pushing the concept of tight MFA integration.
Currently, researchers and early technology adopters attempt to integrate new sensors to be utilized in
MFA systems.
2.2.1. Behavior Detection
Back in time, behavior recognition was utilized to analyze military telegraph operator’s typing
rhythm to track the movement of the troops [
115
]. Today, gestures for authentication purposes
may range from conventional to “hard-to-mimic” ones, since motor-programmed skill results in the
movement being organized before the actual execution [116].
A modern example of such identification is the process of tapping the smartphone
screen [117,118]
.
This approach could be easily combined with any text-input authentication methods as a typing pattern
is unique for each person [
119
121
]. In case the MFA system is specifically developed for predefined
gesture analysis [
122
], the user is required to replicate a previously learned movement while holding
or wearing the sensing device [123125].
A natural step of authentication for widely used handheld and wearable devices is the utilization
of accelerometer fingerprinting [
126
,
127
]. For instance, each smartphone holder could be verified
based on the gait pattern by continuously monitoring the accelerometer data that is almost impossible
to fake by another individual [128].
Cryptography 2018,2, 1 7 of 31
For in-vehicle authentication, the integral system is expected to monitor the driver-specific
features [
129
,
130
], which could be analyzed from two perspectives: (i) vehicle-specific behavior:
steering angle sensor, speed sensor, brake pressure sensor, etc. [
131
,
132
]; and (ii) human factors: music
played, calls made, presence of people in the car, etc. [
133
]. Another important blocker-factor is alcohol
sensor. The engine start function could be blocked in case when the level of alcohol in the cabin is
above an acceptable legal limit [134].
2.2.2. Beam-Forming Techniques
From the telecommunication perspective, Radio-frequency Identification (RFID) and Near-Field
Communication (NFC) techniques have already observed widespread adoption and acceptance
within the community [
135
]. Recent trends in physical-layer security claim that utilizing wireless
Multiple-Input and Multiple-Output (MIMO) solutions to locate the source of the signal may become
a significant breakthrough in validating the token on the user body [136138].
2.2.3. Occupant Classification Systems (OCS)
Some vehicular systems already have the OCS solutions integrated in consumer cars [
139
].
A system of sensors can detect who is currently in the passenger/driver seat by utilizing, for example,
weight or posture and automatically adjusting the vehicle to personal needs [140142].
2.2.4. Electrocardiographic (ECG) Recognition
ECG data could be collected from the user’s smart watch or activity tracker and compared with
an individually stored pattern [
143
,
144
]. The main benefit of using this factor for authentication is that
ECG signals emerge as a potential biometric modality with the advantage of being difficult (or close to
impossible) to mimic. The only way is by utilizing the existing personal recording [145].
2.2.5. Electroencephalographic (EEG) Recognition
This solution is based on the brain waves analysis and could be considered from the fundamental
philosophical proposition “Cogito ergo sum” by R. Descartes, or “I think, therefore I am” [
146
].
It allows for obtaining a unique sample of the person’s brain activity pattern [
147
]. Formerly, EEG data
capture could have been performed only in clinical settings by using invasive probes under the skull
or wet-gel electrodes arrayed over the scalp. Today, the simple EEG collection is possible by utilizing
market-available devices having the size of a headset [148].
2.2.6. DNA Recognition
Human cell lines are an essential resource for research, which is most frequently used in reverse
genetic approaches or as in vitro models of human diseases [
149
]. It is also a source of unique DNA
fingerprinting information [
106
]. Even though the process is time-consuming and expensive, it may be
potentially utilized to pre-authorize the user to the highly secure facility along with other factors.
Subsequently, a comparison of the main indicators for the already deployed and emerging
factors [
150
] is given in Table 1. The factors/sensors are evaluated based on the following parameters:
Universality stands for the presence of factor in each person;
Uniqueness indicates how well the factor differentiates one person from another;
Collectability measures how easy it is to acquire data for processing;
Performance indicates the achievable accuracy, speed, and robustness;
Acceptability stands for the degree of acceptance of the technology by people in their daily life;
Spoofing indicates the level of difficulty to capture and spoof the sample.
Cryptography 2018,2, 1 8 of 31
Table 1. Comparison of suitable factors for MFA: H—high; M—medium; L—low; n/a—unavailable.
Factor Universality Uniqueness Collectability Performance Acceptability Spoofing
Password n/a L H H H H
Token n/a M H H H H
Voice M L M L H H
Facial H L M L H M
Ocular-based H H M M L H
Fingerprint M H M H M H
Hand geometry M M M M M M
Location n/a L M H M H
Vein M M M M M M
Thermal image H H L M H H
Behavior H H L L L L
Beam-forming n/a M L L L H
OCS n/a L L L L M
ECG L H L M M L
EEG L H L M L L
DNA H H L H L L
However, many other issues are to be addressed while integrating the MFA for the end users.
In the following section, we elaborate on those challenges and formalize the recommendations for
improved ease of integration.
3. MFA Operation Challenges
An integration of novel solutions has always been a major challenge for both developers and
managers. The key challenges are presented in Figure 3. In the first place, user acceptance is a
critical aspect for the adoption of strong identity and multi-factor authentication. While adopting
and deploying MFA solutions, it is required to follow a careful and thorough approach—where most
challenges arise from opportunities and potential benefits [151].
3.1. Usability
The main usability challenges emerging in the authentication process could be characterized from
three perspectives [152]:
Task efficiency—time to register and time to authenticate with the system;
Task effectiveness—the number login attempts to authenticate with the system;
User preference—whether the user prefers a particular authentication scheme over another.
In addition to the approaches discussed previously, researchers have already started an investigation
of more specific effects in the authentication procedures based on a variety of human factors.
The authors of [
153
] provided a study on how the user age affects the task efficiency in cases of PIN
and graphic access mechanisms. It is concluded that younger generation can spend up to 50 percent
less time to pass the authentication procedure in both cases. Interestingly, the authors of [
154
] have
shown that gender, in the same case, does not affect the results.
Another direction in the authentication mechanisms usability is related to cognitive properties
of the selected human [
155
]. The work in [
156
] offered an overview on how to make the passwords
memorable while keeping them relatively usable and secure at the same time. Paper by Belk et al. [
157
]
delivered a research on the task completion efficiency and effectiveness among the conventional
passwords and the realistic ones. The results revealed that, for most of the participants, the utilization
of graphic passwords requires more time than for the textual ones. However, cognitive differences
between users, i.e., being Verbal or Imager [
152
], affect the task completion significantly. Here, Verbals
complete the text-based tasks faster than Imagers and vice versa. The work by Ma et al. [
158
] studied
Cryptography 2018,2, 1 9 of 31
the impact of disability (Down syndrome) in the same two scenarios. It was once again confirmed that
textual passwords are utilized better compared to the graphical ones.
MFA Challenges
Security
Data spoofing
Input, transmission security
Social engineering
Privacy
Resistance against known attacks
Investigation of potential attacks
Template protection
Probabilistic behavior
Biometric probabilistic
FAR, FR R, F TE, FTA
Robustness
Resistance against noise
Input device quality
Reliability
Usability
Task efficiency, effectiveness
User preferences
Age, cognitive abilities
Quality of input device
Special disabilities
Integration
New hardware, software
Systems interoperability
Vendo r in depen de ncy
Access to source code
Figure 3. Main operational challenges of MFA.
In addition, the properties of the authentication device play a major role in this process.
The authors of [
159
] investigated the usability of textual passwords on mobile devices. It was proven
that using a smartphone or other keyboardless equipment for creating a password suffers from poor
usability as compared to conventional personal computers. Another work [
160
] confirmed the same
theory from a task efficiency perspective.
Today, most of the online authentication services are knowledge-based [
161
], i.e., depend on
the username and password combination. More complex systems require the user to interact with
additional tokens (one-time passwords, code generators, phones, etc.). Complementing traditional
authentication strategies, MFA is not feasible without biometrics. From this perspective, the work
in [
62
] provided an analysis on how gamification and joy can positively impact the adoption of new
technology. The gesture-related user experience research conducted in [
162
] showed that security and
user experience do not necessarily need to contradict one other. This work also promoted pleasure
as the best way for fast technology adoption. The reference [
163
] addressed the usability of the ECG
solution for authentication, and it was concluded that the application of ECG is not yet suitable for
dynamic real-life scenarios.
Many researchers promoted the utilization of personal handheld devices to be utilized during
the MFA procedure. Michelin et al. [
164
] proposed using the smartphone’s camera for facial and iris
recognition while keeping the decision-making in the cloud. Another work on biometric authentication
for an Android device [
165
] demonstrated an increased level of satisfaction related to higher task
efficiency achieved with the MFA solution. Reference [
166
] studied the usability and practicality of
biometric authentication in the workplace. It was concluded that the ease of technology utilization
and its environmental context play a vital role—the integration and the adoption will always incur
additional and unexpected resource costs.
An extremely important problem of MFA usability roots in the fact that “not all users can use any
given biometric system” [
167
]. People who have lost their limb due to an accident may not be able to
authenticate using a fingerprint. Visually impaired people may have difficulties using the iris-based
authentication techniques.
Biometric authentication requires an integration of new services and devices that results in the
need for additional education during adoption, which becomes more complicated for seniors and
due to related understandability concerns. One fact is clear—user experience plays a prominent role in
Cryptography 2018,2, 1 10 of 31
successful MFA adoption; some say, “user comes first” [
168
]. Today, research in usable security for
knowledge-based user authentication is in the process of finding a viable compromise between the
usability and security—many challenges remain be addressed and will arise soon.
3.2. Integration
Even if all the usability challenges are resolved during the development phase, integration brings
further problems from both technological and human perspectives.
Most of the consumer MFA solutions remain hardware-based [
52
]. Generally, “integrating physical
and IT security can reap considerable benefits for an organization, including enhanced efficiency and
compliance plus improved security” [
169
]. However, convergence is not so simple. Related challenges
include bringing the physical and the IT security teams together, combining heterogeneous system
components, and upgrading the physical access systems.
While developing the MFA system, biometrics independence should be considered carefully, i.e.,
assurance of interoperability criteria should be met [
170
]. The framework needs to have functionality
to handle the biometric data from sensors other than the initially deployed ones [
171
]. The utilization
of multi-biometrics, that is, simultaneous usage of more than one factor should also be taken into
account [172].
Another major interoperability concern is vendor dependency [
173
]. Enterprise solutions are
commonly developed as stand-alone isolated systems that offer an extremely low level of flexibility.
Integration of newly introduced to the market sensors would require complicated and costly updates,
which most probably will not be considered soon.
Further, it should be noted that most of the currently available MFA solutions are not
fully/partially open source. This introduces the questions of trustworthiness and reliability to the third
party service providers. The available level of transparency delivered by both hardware and software
vendors should be taken into consideration while selecting the MFA framework in the first place.
3.3. Security and Privacy
Any MFA framework is a digital system composed of critical components, such as sensors, data
storage, processing devices, and communication channels [
174
]. All of those are typically vulnerable to
a variety of attacks at entirely different levels, ranging from replay attempts to adversary attacks [
175
].
Security is thus a necessary tool to enable and maintain privacy. Therefore, we begin with the attacks
executed on the input device itself [
176
]. Letting only the legitimate controller access and process
sensitive personal data exposes the community to the main risks related to MFA security that are
listed further.
The first of the key risks is related to data spoofing that would be successfully accepted by the
MFA system [
177
]. Notably, due to biometrics being used by a variety of MFA frameworks, a glaring
opportunity for the attacker to analyze both the technology underlying the sensor and the sensor
itself results in revealing the most suitable spoofing materials. The main goal of the system and
hardware architects is to provide either a secure environment or, in case it is not possible, to consider
the related spoofing possibilities in advance. A risk of capturing either physical or electronic patterns
and reproducing them within the MFA system should be addressed carefully.
Conventionally, the safeguard to protect against electronic replay attacks requires utilization
of a timestamp [
178
]. Unfortunately, a biometric spoofing attack is fairly simple to execute [
179
].
Even though biometrics can improve the performance of the MFA system, they can also increase the
number of vulnerabilities that can be exploited by an intruder. Further risk is sensitive data theft
during the transmission between the sensor and the processing/storage unit. Such theft may primarily
occur due to insecure transmission from the input device through extraction and matching blocks to
the database, and there is potential for an attack [
180
]. The required levels of data safety should be
guaranteed to resist against this risk type [181,182].
Cryptography 2018,2, 1 11 of 31
Another opportunity to attack the MFA system is by capturing the secret data sample [
183
].
For knowledge factors, the system would be immediately compromised in case zero-knowledge
solutions are not utilized [
184
]. Specific interest is dedicated to capturing a biometric sample that could
not be updated or changed over time [
185
]. Hence, protection of the biometric data requires a higher
level of security during capture, transmission, storage, and processing phases [186].
The following risk is related to the theft from the data storage. Conventionally, databases are
stored in a centralized manner, which offers a single point of failure [
187
,
188
]. At the same time, some
of the remote systems contacting the database are not always legitimately authorized to access the
personal data stored. High level of isolation is required to protect the data from theft in addition to
utilizing irreversible encryption [
189
]. Subsequent risk is related to location-related attacks. The GPS
signal could be vulnerable to position lock (jamming) or to feeding the receiver with false information,
so that it computes an erroneous time or location (spoofing) [
190
,
191
]. Similar techniques may be
applied to cellular- and WLAN-based location services [192,193].
Finally, being an information technology system, MFA framework should deliver relatively high
levels of “throughput” [
194
], which reflects the capability of a system to meet the needs of its users in
terms of the number of input attempts per time period [
195
]. Even if the biometrics are considered
suitable in every other aspect, but the system can only perform, e.g., one biometrics-based match
per hour, whereas it is required to operate at 100 samples per hour, such a solution should not be
considered as feasible. The recommendation here is to select appropriate processing hardware for the
server/capture side.
The MFA security framework should also support a penetration testing panel to assess its potential
weaknesses. Today, the developers are often conducting external audit to evaluate the risks and act
based on such evaluation for more careful planning. The MFA system should thus be assessed to
deliver a more secure environment.
3.4. Robustness to Operating Environment
Even if the security and privacy aspects are fully resolved, the biometric systems, mainly
fingerprinting, were falling short of fulfilling the “robustness” requirement since the very beginning of
their journey [
196
]. This was mainly due to the operational trials being conducted in the laboratory
environment instead of the field tests. One distinct example is voice recognition, which was highly
reliable in a silent room but failed to verify the user in urban landscapes.
A similar problem applies to early facial recognition techniques, which failed to operate without
adequate light support, quality camera, etc. [
197
]. The flip side of the coin was the need for continuous
supervision of the examined subject. Even today, there are either bits of advice on where to
look/place fingers, or there is visual aid available during the security check. The lack of experience in
machine-to-human interaction is commonly analyzed with Failure to Enroll (FTE) as well as Failure
to Acquire (FTA) rates [
198
]. They both depend on the users themselves as well as the additive
environmental noise.
Since a significant part of MFA is highly dependent on biometry, it could be classified as inherently
probabilistic due to such nature [
199
]. The base of the biometric authentication lies in the field of pattern
matching, which in turn relies on approximation. Approximate matching is a critical consideration
in any MFA system, since difference between users could be crucial due to a variety of factors and
uncertainty. The image captured during a fingerprint scan would be different every time it is observed
because of the presentation angle, pressure, dirt, moisture, or differentiation of sensors even if taken of
the same person.
Two important error rates used to quantify the performance of a biometric authentication system
are FAR and FRR. FAR is the percentage of impostors inaccurately allowed as genuine users. It is
defined as the ratio of the number of false matches to the total number of impostor match attempts. FRR
is the number of genuine users rejected from using the system, which is defined as the ratio of the
number of false rejections to the total number of genuine match attempts.
Cryptography 2018,2, 1 12 of 31
Literature further recommends the utilization of the Crossover Error Rate (CER) in addition to the
previously discussed metrics [
200
]. This parameter is defined as the probability of the system being in
a state where FAR equals to FRR. The lower this value is, the better the system performs. According
to [
201
], “Higher FAR is preferred in systems where security is not of prime importance, whereas
higher FRR is preferred in high-security applications”. The point of equality between FAR and FRR is
referred to as Equal Error Rate (EER) [
202
]. Based on the above, it could be once again concluded that
a system utilizing solely biometrics may not be considered as a preferred MFA framework.
By analyzing the above listed challenges, it is possible to evaluate and assess the entire MFA
system. In what follows, we propose an approach to enable MFA for vehicular integration based on
the availability of a large number of sensors in modern vehicles.
4. Enabling Flexible MFA Operation
In this work, we offer a new authentication scheme that focuses on the vehicle-to-everything (V2X)
scenarios, since cars of today are already equipped with multiple sensors that could potentially be
utilized for MFA. Conventionally, the user has a username/password/PIN/token [
203
] and will
additionally be asked to utilize a biometric factor, such as facial features or fingerprints. The general
overview supported by a follow-up discussion is given in Figure 4. If the authentication procedure fails
to establish trust by using this combination of factors, then the user will be prompted to authenticate
by utilizing another previously registered factor or a set of those. This MFA system may not only
verify the accuracy of the user input but also determine how the user interacts with the devices, i.e.,
analyze the behavior. The more the user interacts with the biometric system, the more accurate its
operation becomes.
Figure 4. Current and emerging MFA sensors for vehicles.
Another feature of the discussed scenario is the actual sensor usability in case of interaction with
a car [
204
]. If a sensor (e.g., a fingerprint reader) is being utilized and that device is not available from
where the user is attempting to log in or gain access—the user experience becomes inadequate. Having
a dual-purpose device—smartphone or smartwatch (suitable for executing the information security
primitives [
205
]), which the user already has in his or her possession—as an additional MFA factor
(not only as a token) makes both the system costs and usability much more reasonable [206].
The presence of large amounts of sensor data brings us to the logical next step of its application in
MFA. We further envision potential utilization of the corresponding factors to authenticate the user
Cryptography 2018,2, 1 13 of 31
without implementing a dedicated “verifier” with the actual biometric data except for the one collected
in real time.
4.1. Conventional Approach
One of the approaches considered within the scope of this work is based on utilizing Lagrange
polynomials for secret sharing [
207
]. The system secret
S
is usually “split” and distributed among a set
of key holders. It could be recovered later on, as described in [
208
210
] and numerous other works, as
f(x) = S+a1x+a2x2+· · · +al1xl1,
f(0) = S,(1)
where
ai
are the generated polynomial indexes and
x
is a unique identification factor
Fi
. In such
systems, every key holder with a factor ID obtains its own unique key share SID =f(I D).
In conventional systems, it is required to collect any
l
shares
{SID1
,
SID2
,
. . .
,
SIDl}
of the initial
secret to unlock the system, while the curve may offer
n>l
points, as it is shown in Figure 5. The basic
principle behind this approach is to specify the secret
S
and use the generated curve based on the
random coefficients
ai
to produce the secret shares
Si
. This methodology is successfully utilized in
many secret sharing systems that employ the Lagrange interpolation formula [211,212].
x=0 F1F2FlFl+1 Fn
Secret value
S
S1
S2
Sl
Sl+1
Sn
S
f(x)
Figure 5. Lagrange secret sharing scheme.
Unfortunately, this approach may not be applied for the MFA scenario directly [
213
], since the
biometric parameters are already in place, i.e., we can neither assign a new
Si
to a user nor modify
them. On the one hand, the user may set some of the personal factors, such as password, PIN-code, etc.
On the other hand, some of them may be unchangeable (biometric parameters and behavior attributes).
In this case, an inverse task where the shares of the secret
SIDi
are known as factor values
Si
is to be
solved. Basically,
Si
are fixed and become unique
{S1
,
S2
,
. . .
,
Sl}
when set for a user. In this case,
S
is
the secret for accessing the system and should be acquired with the user factor values. A possible
solution based on the reversed Lagrange interpolation formula is proposed in the following subsection.
4.2. Proposed Reversed Methodology
In this work, we consider the MFA system with explicit
l
factors
F
. Each factor
Fi
has a unique
secret
Si
obtained with the corresponding procedure (PIN, fingerprint, etc.) from the user. In the
worst case, it is related to the biometric data—the probability that it changes over time is low.
The corresponding factors and secrets could then be represented as
Cryptography 2018,2, 1 14 of 31
F1:S1,
F2:S2,
. . .
Fl:SL,
Fl+1:T,
(2)
where
Si
is the secret value obtained from the sensor (factor),
l
is the number of factors required to
reconstruct the secret, and Fl+1is a timestamp collected at time instant T.
It is important to note that providing the actual secrets to the verifier is not an option, especially in
case of sensitive biometric data, because a fingerprint is typically an unchangeable factor. Hence, letting
even a trusted instance obtain the corresponding data is a questionable step to make. Conversely,
compared to the method considered in Section 4.1, the modified algorithm implies that
Si
are obtained from
the factors (only one polynomial describes the corresponding curve), as it is shown in Figure 5. In other
words, the proposed methodology produces the system secret
S
based on the collected factor values
Si
instead of assigning them in the first place.
A system of equations connected to the Lagrange interpolation formula with the factors, their
values, and the secret for the system access is
S1=S+a1F1+a2F2
1+· · · +al1Fl1
1+alFl
1,
S2=S+a1F2+a2F2
2+· · · +al1Fl1
2+alFl
2,
. . .
Sl=S+a1Fl+a2F2
l+· · · +al1Fl1
l+alFl
l,
T=S+a1T+a2T2+· · · +al1Tl1+alTl,
(3)
where
ai
are the corresponding generated coefficients,
f(x) = S+a1x+a2x2+· · · +al1xl1
, and
f(
0
) = S
. The system in Equation (3) has only one solution for
S
and it is well known from the
Lagrange interpolation formula.
Lemma 1.
One and only one polynomial curve
f(x)
of degree
l
1could be described by
l
points on the plane
(x1,y1),(x2,y2), . . . , (xl,yl)
fx=a0+a1x+. . . +al1xl1,{f(xi) = yi}l
i=1.
Hence, the system secret
S
may be recovered based on
l
collected shares as given by the
conventional Lagrange interpolation formula without the need to transfer the original factor secrets
Si
to the verifier. Hence, the sensitive person-related data is kept private, as
S= (1)ll+1
i=1
Si
l+1
j=1, j6=i
Fj
FiFj
, (4)
where
Fl+1=T
. The proposed modifications are required to assure the uniqueness of the acquired
data, see Figure 6.
Due to the properties of the Lagrange formulation, there can only be one curve described by
the corresponding polynomial (Lemma 1); therefore, each set of
[Fi:Si]
will produce its unique
S
.
However, if the biometric data collected by MFA has not been changed over time, the secret will always
remain the same, which is an obvious vulnerability of the considered system. On the other hand, a
simple addition of the timestamp should always produce a unique curve, as it is shown in Figure 6
for T,T1, and T2.
Cryptography 2018,2, 1 15 of 31
x=0 F1F2FlFT
Secret value
S
S1
S2
Sl
ST
ST1
ST2
T
T1
T2
Time factor
Figure 6. Reversed method based on the Lagrange polynomial.
The proposed solution provides robustness against the case where all
Si
remain unchanged over
time. This is achieved by adding a unique factor of time
T
, which enables the presence of
Fl
with
the corresponding secret. It is necessary to mention that the considered threshold scheme based on
the Lagrange interpolation formula utilizes Rivest–Shamir–Adleman (RSA) mechanism or ElGamal
encryption/decryption algorithm for authentication during the final step. In this case, it is proven that
we obtain a secure threshold scheme related to secrets Siin [214].
4.3. Proposed MFA Solution for V2X Applications
Indeed, our proposed solution may operate out-of-the-box in case where all
l
factors are present.
The system may thus provide a possibility to identify and report any outdated factor information—for
example, weight fluctuation [
215
]. Access to a service could be automated when some of the factors
are not present [216]. We further elaborate on this feature in the current subsection.
4.3.1. Factor Mismatch
Assuming that the number of factors in our system is
l=
4, the system secret
S
can be represented
in a simplified way as a group of
ShF1F2F3F4i.
Here, if any of
Si
are modified—the secret recovery mechanism would fail. An improvement to
this algorithm is delivered by providing separate system solutions
Si
for a lower number of factors
collected. Basically, for
l=
3, the number of possible combinations of factors with one missing is
equal to four, as follows
S1hF1F2F3i,
S2hF1F3F4i,
S3hF1F2F4i,
S3hF2F3F4i. (5)
Cryptography 2018,2, 1 16 of 31
The device may thus grant access based on a predefined risk function policy. As the second
benefit, it can inform the user (or the authority) that a particular factor
Fi
has to be updated based on
the failed
Si
combination. Indeed, this modification brings only marginal transmission overheads, but,
on the other hand, enables higher flexibility in authentication and missing factor validation.
4.3.2. Cloud Assistance
Another important scenario for MFA is potential assistance of the trusted authority in
Fi:Si
mismatch or loss. In case when the user fails to present a sufficient number of factors, the trusted
authority can be requested to provide the temporary factor keys, as it is demonstrated in Figure 7.
x=0 F1F2FlFT)1)2
Secret value
S
S1
S2
Sl
ST
S)1
S)2
Time factor
Figure 7. Trusted authority assistance in authentication when user is missing two factors.
For example, assume that the user forgot or lost two factors
F2
and
F3
with the corresponding
keys
S1=f(F1)and S2=f(F2)
. The trusted authority is willing to assist in authentication—two
temporary keys
SΦ1=f(Φ1)
and
SΦ2=f(Φ2)
are thus generated and sent to the user via a
secure channel. Obtaining these keys and applying the Lagrange interpolation formula with RSA or
ElGamal encryption/decryption-based threshold authentication procedure involves the following
factors and keys
F1:S1,
F2:S2,
. . .
Fl:SL,
Fl+1:T,
Φ1:SΦ1,
Φ2:SΦ2,
(6)
as described in [214]. This allows for gaining access to the device.
The proposed solution is designed explicitly to complete the MFA step of the authentication,
that is, its usage for SFA and 2FA is not recommended. This is mainly due to the features of the
Lagrange interpolation formula. Basically, in the SFA case and without the
Fl+1:T
factor, the equation
at hand can be simply represented as
S1=S+b1F1
, i.e., it will become ‘a point’. Even adding a
random timestamp factor will not provide any valuable level of biometric data protection, since an
eavesdropper could be able to immediately recover the factor secret.
Cryptography 2018,2, 1 17 of 31
The above is not suitable for the 2FA either, since providing two factors allows the curve to have
linear behavior, i.e., the eavesdropper is required two attempts to recover the secrets. However, adding
a timestamp factor here allows for providing the necessary level of safety with three actual factors,
as discussed below.
4.4. Potential Evaluation Techniques
Conventionally, authentication systems utilizing only the knowledge of ownership factors operate
in pass/fail mode, i.e., the input data is either correct or incorrect. When it comes to using biometrics,
the system faces potential errors during the biometric sample capturing, which was discussed
previously in Section 3.4. We further elaborate on our proposed methodology from the crucial
FAR/FRR perspective.
Typically, the FAR/FRR parameters of a sensor are provided by vendors based on the statistically
collected data [
217
]. For the MFA framework, we assume two possible decisions made during the user
authentication phase, as it is displayed in Figure 8: (i)
H0
—the user is not legitimate; or (ii)
H1
—the
user is legitimate. These form the entire sample space of
P(H0) + P(H1) =
1. The risk policy is
assumed to be handled by the authentication system owner who also sets up the distributions of
P(H0)
and P(H1).
MFA processor Decision
Sensing devices
Accept
Reject
Sensor 1Sensor 1
Sensor 1Sensor 2
Sensor 1Sensor n
z , FAR , FRR
1 1 1
z , FAR , FRR
2 2 2
TH
z , FAR , FRR
n n n
Bayes estimator
P(H0), P(H1), P
Input
...
Figure 8. MFA system mode. PTH is the selected threshold.
Generalizing, there might be
n
biometric sensors collecting the user input data. Each individual
sensor measurement from the set
Z={z1, . . . , zn}
is distributed within
[0, 1]
, and this set is further
analyzed under the conditions of two previously considered hypotheses. The measurements delivered
from the sensors could be processed in two different ways as introduced in the sequel.
4.4.1. Strict Decision Methodology
Each sensor decides whether the user is legitimate or not by returning either accept or reject.
The MFA system then combines the collected results and provides a group decision based on the
resulting vector. Hence, it is possible to utilize the threshold decision functions or weighted threshold
functions depending on the reliability of the sensor.
For the first case, the sensor will return the value
zi
,
zi= [
0; 1
]
, which could be interpreted as
either YES or NO. Then, the conditional probabilities
P(zi|H0)
and
P(zi|H1)
are defined by
FARi
and
FRRi
values, respectively, for
i
-th sensor. Here,
FARi
and
FRRi
are taken at the CER/EER point,
e.g.,
zi
is selected at the point where
FARi=FRRi
. Generally, this methodology reflects the scenarios
of ownership or knowledge factors from the biometric perspective.
Cryptography 2018,2, 1 18 of 31
4.4.2. Probabilistic Decision Methodology
The sensor responds with a result of its measurements as well as a probabilistic characteristics.
Further, the data is merged before the final decision is made. Therefore, the entire set of the measured
data could be utilized when making a group decision and, accordingly, a common result might be
established based on the set collected from all sensors.
In the second case, the sensor returns a result of the measurements as well as the template
comparison in the form of a match score
zi(
0
zi
1
)
. For each of the values
zi
, the conditional
probability
P(zi|H0)
is calculated based on the
FARi
values at
zi
. In addition, the conditional
probability P(zi|H1)is determined by FRRivalues at zi.
This approach offers an opportunity to consider the strict decision methodology as a simplified
model of the probabilistic one for the case where
FARi
and
FRRi
are given only in one point. Here, the
measurement result can only take two values, i.e., higher or lower than the selected threshold.
4.4.3. Evaluation
In this work, we consider a more general case of the probabilistic decision-making methodology,
while a combination of the measurement results for the individual sensors is made similarly to the
previous works by using the Bayes estimator [
218
]. Since the outcomes of measurements have a
probabilistic nature, the decision function is suitable for the maximum a posteriori probability solution.
In more detail, the decision function may be described as follows. At the input, it requires a
conditional probability of the measured value from each sensor
P(zi|H0)
and
P(zi|H1)
together
with a priori probabilities of the hypotheses
P(H0)
and
P(H1)
. The latter values could be a part of
the company’s risk policy as they determine the degree of confidence for specific users. Then, the
decision function evaluates the a posteriori probability of the hypothesis
P(H1|Z)
and validates that
the corresponding probability is higher than a given threshold PTH.
The measurement-related conditional probabilities can be considered as independent random
variables; hence, the general conditional probability is as follows:
PZ
HJ=
ziZ
Pzi
HJ,J{0; 1}. (7)
Further, the total probability P(Z)is calculated as
P(Z) =
ziZ
P(zi|H0)P(H0) +
ziZ
P(zi|H1)P(H1), (8)
where
Pzi
HJ
,
J{0; 1}
are known from the sensor characteristics, while
P(H0)
and
P(H1)
are a
priori probabilities of the hypotheses (a part of the company’s risk policy).
Based on the obtained results, the posterior probability for each hypothesis HJ,J{0; 1}can be
produced as
P(H1|Z)=ziZP(zi|H1)P(H1)
P(Z). (9)
For a comprehensive decision over the entire set of sensors, the following rule applies
P(H1|Z)>PT H ⇒ {Accept}, else {Reject}. (10)
As a result, the decision may be correct or may lead to an error. The FAR and FRR values could
then be utilized for selecting the appropriate threshold PTH based on all of the involved sensors.
5. Discussion and Future Prospects
Today, authentication matters more than ever before. In the digital era, most users will rely on
biometrics in matters concerning systems security and authorization to complement the conventional
Cryptography 2018,2, 1 19 of 31
passwords. Even though privacy, security, usability, and accuracy concerns are still in place, MFA
becomes a system that promises the security and ease of use needed for modern users while acquiring
access to sensitive data.
Without a doubt, biometrics are one of the key layers to enable the future of MFA.
This functionality is often regarded not standalone but as a supplement to traditional authentication
approaches like passwords, smart cards, and PINs. Combining two or more authentication mechanisms
is expected to provide a higher level of security when verifying the user. The expected evolution
towards MFA is rooted in the synergistic biometric systems that allow for significantly improved
user experience and MFA system throughput, which would be beneficial for various applications (see
Figure 9). Such systems will intelligently couple all three factor types, namely, knowledge, biometrics,
and ownership.
Flight AY-705
Passport
Fingerprint
Facial
BehaviorWeight
Voice
ECG
Smartphone
Figure 9. Biometric MFA for the airport scenario.
Since conventional single-factor systems of today are based on only one parameter (unimodality
property), if its acquisition is affected in any way (be it noise or disruption), the overall accuracy will
degrade. As a reminder, collecting a single type of non-knowledge related data, e.g., biometrics, could
exclude part of the user population when particular disabilities are present. Moreover, spoofing this
only factor is a relatively simple task.
One of the most promising directions in MFA is behavior-based biometrics providing entirely
new ways of authenticating the users. The solutions that are based on muscular memory, e.g., writing
or gestures, coupled with machine learning become more prominent examples. Already today,
software can extrapolate user handwriting and reach the confidence levels of above 99.97 percent [
219
].
More forward-looking MFA sources to be utilized in the nearest future are heart and brain [
220
].
The attractive area of ECG and EEG analysis is also expected to provide unique identification samples
for each subject.
Another military-inspired research activity already shows the capability to identify the users
based on the way they interact with computer [
221
]. This approach takes into consideration the typing
speed, typical spelling mistakes, writing rhythm, and other factors [
222
]. The appropriate terminology
is not settled yet, and some call this methodology Passive Biometrics [
223
], while others name it
Continuous Authentication [
224
]. It results in having a unique fingerprint of the user–computer
interaction pattern, which is extremely difficult to replicate.
All of the discussed MFA scenarios require significant memory resources to statistically analyze the
input data and store the biometric samples even if utilizing different optimization techniques [
225
,
226
].
Cryptography 2018,2, 1 20 of 31
A very promising direction of the MFA development is therefore in the area of neural networks and
Big Data [
227
]. Here, many successful applications have been known to the community for more
than a decade. Examples could be found in [
228
230
] where conventional factors, such as iris, retina,
fingerprints, etc., are considered. Utilizing neural networks for the next-generation biometrics is the
most likely way to proceed due to presently high levels of the analysis complexity [231,232].
In summary, biometric technology is a prominent direction driven by the mobile device market.
The number of smartphones to be sold only in the US is expected to reach 175 million units by 2018
with the corresponding market to exceed $50.6B in revenues by 2022 [
233
,
234
]. It is believed that a
strong push towards the utilization of biometrics in many areas of life is imminent, since most of the
flagman devices are already equipped with the fingerprint scanner and facial recognition technology
in addition to convention PIN codes.
This work provided a systematic overview of the state-of-the-art in both technical and usability
issues, as well as the major challenges in currently available MFA systems. In this study, we discussed
the evolution of authentication from single- through two- and towards multi-factor systems. Primarily,
we focused on the MFA factors constituting the state-of-the-art, future possible directions, respective
challenges, and promising solutions. We also proposed an MFA solution based on the reversed
Lagrange polynomial as an extension of Shamir’s Secret Sharing scheme, which covers the cases of
authenticating the user even if some of the factors are mismatched or absent. It also helps qualify the
missing factors without disclosing the sensitive data to the verifier.
Acknowledgments: The work of the second author is supported by the Academy of Finland.
Author Contributions:
A.O. prepared the state-of-the-art; N.M. and T.M. conducted the analysis of challenges;
S.B. designed the flexible MFA solution; A.O., S.B., N.M., S.A., T.M, and Y.K. wrote the paper.
Conflicts of Interest: The authors declare no conflict of interest.
Abbreviations
MFA Multi-Factor Authentication
SFA Single-Factor Authentication
2FA Two-Factor Authentication
SSS Shamir’s Secret Sharing
PIN Personal Identification Number
ID Identification Number
ATM Automated Teller Machine
FAR False Accept Rate
FRR False Reject Rate
PPG Photoplethysmography
RFID Radio-Frequency Identification
NFC Near-Field Communication
OCS Occupant Classification Systems
ECG Electrocardiography
EEG Electroencephalography
GPS Global Positioning System
FTE Failure to Enroll
FTA Failure to Acquire
CER Crossover Error Rate
EER Equal Error Rate
V2X Vehicle-to-Everything
IAM Identity and Access Management
Cryptography 2018,2, 1 21 of 31
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... The latest studies [10,11] have shown that the use of behavioral biometrics has become an increasingly popular part of MFA (Multi-Factor Authentication) [12]. Over the last couple of years, institutions for confidential data handling have been more prone to reach for users' behavioral patterns (e.g., keyboard strokes, mouse movements, or mobile device handling) when implementing identity theft countermeasures, as this sort of data does not require any additional user involvement harmful for the UX (User Experience) [13]. ...
... In order to ensure reliable training and testing datasets, only those users with more than a certain number of unique sessions ( 12), and ones for which a model could be built (undamaged data) were considered. Each viable session had to last for a fixed amount of time or longer ( > = 100 s) to assure the occurrence of at least = 5 windows lasting for . ...
... In order to correctly evaluate the session, the final score-consisting of M sub-scores-was calculated as their average (11). Whenever the final score exceeded the user-defined threshold (evaluated based on certain business requirements of the bank), the session was considered fraudulent (12). ...
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Cybersecurity companies from around the world use state-of-the-art technology to provide the best protection against malicious software. Recent times have seen behavioral biometry becoming one of the most popular and widely used components in MFA (Multi-Factor Authentication). The effectiveness and lack of impact on UX (User Experience) is making its popularity rapidly increase among branches in the area of confidential data handling, such as banking, insurance companies, the government, or the military. Although behavioral biometric methods show a high degree of protection against fraudsters, they are susceptible to the quality of input data. The selected behavioral biometrics are strongly dependent on mobile phone IMU sensors. This paper investigates the harmful effects of gaps in data on the behavioral biometry model’s accuracy in order to propose suitable countermeasures for this issue.
... After the constraints definition, we design the strategy to create the DSI. To assure adequate protection during the DSI creation, we implement a two-factor authentication process (2FA) [48]. In particular, we use the ownership factor, something the user has, such as cards, smartphones, or other tokens, and the knowledge factor, something the user knows, such as a password or generically a secret [48]. ...
... To assure adequate protection during the DSI creation, we implement a two-factor authentication process (2FA) [48]. In particular, we use the ownership factor, something the user has, such as cards, smartphones, or other tokens, and the knowledge factor, something the user knows, such as a password or generically a secret [48]. ...
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The rising connection of vehicles with the road infrastructure enables the creation of data-driven applications to offer drivers customized services. At the same time, these opportunities require innovative solutions to protect the drivers’ privacy in a complex environment like an Intelligent Transportation System (ITS). This need is even more relevant when data are used to retrieve personal behaviors or attitudes. In our work, we propose a privacy-preserving solution, called Private Driver DNA, which designs a possible architecture, allowing drivers of an ITS to receive customized services. The proposed solution is based on the concept of Driver DNA as characterization of driver’s driving style. To assure privacy, we perform the operations directly on sanitized data, using the Order Revealing Encryption (ORE) method. Besides, the proposed solution is integrated with ITS architecture defined in the European project E-Corridor. The result is an effective privacy-preserving architecture for ITS to offer customized products, which can be used to address drivers’ behaviors, for example, to environmental-friendly attitudes or a more safe driving style. We test Private Driver DNA using a synthetic dataset generated with the vehicle simulator CARLA. We compare ORE with another encryption method like Homomorphic Encryption (HE) and some other privacy-preserving schemas. Besides, we quantify privacy gain and data loss utility after the data sanitization process.
... However, even with few resources, it is important to properly implement security in IoT [5] systems − in our case the implementation of device authentication. For that, it is necessary to know this specific area's trends, challenges, and opportunities. ...
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The few resources available on constrained devices in Internet of Things are an important issue when we think about security. In this perspective, our work proposes an agile systematic review literature on works involving the Internet of Things, authentication, and Fog Computing. As a result, related works, opportunities, and challenges found at the intersection of those areas were brought, supporting other researchers and developers who work in those areas.
... Therefore these systems can be hacked by using stolen information. In Multi-Factor Authentication (MFA) systems, besides Knowledge-based and Object-based information, also the use of Biometric-based information of a person itself is integrated [3]. The measurement of biometric data is divided into three subdivisions again: Physiological, Behavioral, and Biological-based [4]. ...
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Secure systems and trustworthy access are major concerns in the development of Beyond 5G (B5G) and Sixth Generation (6G) wireless systems. Due to the increasing complexity of new communication systems and the interoperability of different vendors, it is mandatory to exclude the risk of unauthorized access by integrating security concepts from the very beginning. In recent approaches in the research of wireless systems, various specialized solutions are being explored. This includes the seamless integration of localization and sensing along with the development of wireless body near sensors into a universal communication system. The topic of Medical Digital Twins (MDTs) is one aspect that helps improve quality of life by using body-near sensors to monitor biomedical data. This aggregated information also can be used for the authentication of a specific user and to grant access to approved systems. Therefore in this work, a set of Key Performance Indicators (KPIs) is developed to evaluate different aspects in the field of biometric authentication systems. The indicators are identified in the combined study of biometric systems developed in previous works, highlighting both advantages and disadvantages for each proposed biometric method with respect to authentication. These different biometric approaches result in a possible Wireless Body Area Network (WBAN) which is evaluated by using the defined KPIs to identify potential synergistic effects in Multi-Factor Authentication-combinations.
... 2FA is not a silver bullet, but it is still helpful in providing safeguards from many threats. 2FA can protect against replay attacks, brute force attacks, and many social engineering attacks [64], as the password or session information alone is insufficient. There are applications available to assist in 2FA, bypassing issues such as no mobile network. ...
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With the convenience of Cloud computing (CC) comes changes and challenges to cybersecurity. Organisational networks have changed, and the traditional perimeter-style defence is ineffective in CC architecture. Tracking the location of data processes within CC poses challenges to organisations to preserve data privacy (Sun, IEEE Access 7:147420–147452, 2019). Zero trust (ZT) architecture offers a way to use familiar network, cyber, and software security tools in a purpose-fit way to protect data in the Cloud. Probability-based authentication (PBA) uses more identifiers about user entities such as device, location, and activity to help identify bad actors and restrict access (Wiefling S, Lo Iacono L, Dürmuth M, Is this really you? An empirical study on risk-based authentication applied in the wild. In: ICT systems security and privacy protection, Cham, pp 134–148, 2019). This chapter provides an overview of how to apply security and preserve data privacy in the Cloud.KeywordsCloud computingCybersecurityZero TrustData privacyProbability-based authentication
... On the other hand, biometrics Frontiers in Virtual Reality frontiersin.org such as fingerprint, voice recognition, and camera photo ID have proved to be reliable for identity verification in virtual interaction (Semple et al., 2010), many services are already offering multifactor authentication (MFA) (Ometov et al., 2018) using biometrics, such as fingerprint on mobile platforms. The use of VR devices, on the other hand, offers more options for biometrics acquisition than other platforms. ...
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... Multi-factor authentication (MFA) is an authentication method which requires the user to provide more factors of evidence before being authenticated [33], hence enabling a more robust protection than a password, which is the most common solution. ...
... However, for communication metrics, the proposed model was checked in the NS-3 simulation tool. The people interested in this topic are encouraged to overview references [120][121]. ...
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