The object of the research is a biometric authentication system based on neural network transformation of features into a cryptographic key. The analysis of the security of such systems is carried out using the methods of cognitive modeling. The use of the neural network transformation “biometrics-key” can significantly reduce the likelihood of a number of attacks by external intruders due to the distributed storage of the base of biometric images and allows the use of a secret cryptographic key generated on the basis of the image as the output vector of the neural network. To assess the security of the biometric system based on the ML model, an analysis of current threats, vulnerabilities and potential attack vectors was carried out. A fuzzy gray cognitive map is built for modeling and assessing local relative risks of information security in the event of an attacker without using and using the architecture of the ML model of the neural network transformation “biometrics-key”. The indicators of the local relative risk of a system malfunction and refusal to use it (breach of integrity) and modification of the base and ML model (breach of confidentiality) decreased by 45%.
Currently, traditional authentication methods (passwords and IDs) are no longer
sufficient to ensure security - they have been replaced by integrated biometric systems
embedded in an increasing number of devices (for example, FaceID and TouchID
technologies in mobile devices). Today, there are two main areas of application of
biometric methods: solving the problem of user authentication and their integration
with cryptographic systems [
Biometric authentication systems based on the use of artificial intelligence and
machine learning technologies approximate a nonlinear functional display that allows the
recognized biometric image to be attributed to one of the predefined classes. The
machine learning models (ML models) used to solve this problem are very sensitive
to changes in input data, which allows an attacker in some cases to influence the
result of the biometric system by modifying the presented biometric images. A
significant number of services operate on the basis of ML models that process biometric
images, which is an important problem in ensuring information security of the system
as a whole [
The purpose of the work is to provide a cognitive analysis of the security of a biometric authentication system based on a neural network transformation of biometric features into a cryptographic key.
To achieve the purpose, the following tasks were set: ─ analysis of existing biometric cryptographic systems; ─ security assessment of the neural network biometric authentication system based on cognitive modeling technologies. 2
Existing biometric cryptographic systems using facial images as primary biometric features can be divided into three categories according to the nature of the cryptographic key processing (Table 1).
For the subsequent analysis and application of the ML model in solving the
problem of image classification, it is necessary to extract the vector of primary features
from the generated biometric templates [
To generate keys based on biometric images, two main tools are used (Table 3)
that meet the requirements of modern cryptography and have an acceptable estimate
of the magnitude of the second type error [
GOST R 52633-2006†: protection against attacks on the “last bit” of the decision rule.
Advantages the length of the generated key is specified as an algorithm parameter; no need to store a private key, but storage of auxiliary data is required; allows to get a single key from one set of biometric data Disadvantages Training requires significant comput- the quality of work corresponds to ing resources and makes high demands the quality of the applied error coron the quality of the training sample. rection codes; fuzzy extractors are susceptible to the same classes of attacks as fuzzy containers Fig. 1. The scheme of the neural network converter “biometrics-code” (a) and the fuzzy extractor (b). † GOST R 52633-2006 З Information protection. Information protection technology.
Requirements for the means of high-reliability biometric authentication, http://docs.cntd.ru/document/1200048922, last accessed 2021/01/10.
X – the biometric template used during registration, F – the quantization function, R – the random noise introduced into the construction of the secure sketch, P – the generated secure sketch, Y – the biometric template used in the user authentication process.
A generalized scheme of the neural network system of biometric identification and authentication (NSBIA) of a person is shown in Fig. 2 and reflects the main stages of processing biometric information.
To store the database of biometric formed, the parameters of the neural network
connection weights are used, which makes it possible to ensure the confidentiality of
the biometric network system, since even a compromise of the neural network
connection weights will not give the intruder information either about the users of the
system, or the system itself. The only vulnerable element of the system is the output
vector generated by the neural network, which makes it possible to assign the
presented biometric image to one of the known classes. This type of attack on a
biometric system is called an attack on the “last bit” of the decision rule [
Consequently, the use of biometric systems based on this class of neural networks and other ML models with an open vector encoding the belonging of the input image to a certain class becomes problematic in open and weakly protected information systems.
The key for biometric identification and authentication systems are falsification of biometric data presented through the user interface and leakage from the database of biometric images‡. Vulnerabilities in the implementations of biometric identification and authentication systems can be divided into: ─ vulnerabilities in used libraries and plug-ins; ─ vulnerabilities in the program code; ─ architectural vulnerabilities. ─ Attacks on biometric images presented through the system user interface can be divided into two groups: ─ non-targeted attack (a general type of attack when the main target is an incorrect classification result); ─ targeted attack (the goal is to obtain a label of the required class for a given input image§). ─ For systems using machine learning methods and technologies, there are two types of AML attacks (adversarial machine learning)**: ─ evasion – an attacker causes the model to behave incorrectly. The system is viewed by the attacker as a black box. This type of attack is considered the most common ‡ How vulnerable are biometric Big Data systems: causes of errors and their measurement metrics, https://www.bigdataschool.ru/blog/biometrics-vulnerabilities-bigdata-ml.html § Attacks on biometric systems, https://www.itsec.ru/articles/ataka-nabiometricheskie-sistemy ** How to deceive a neural network or what is an Adversarial attack, https://chernobrovov.ru/articles/kak-obmanut-nejroset-ili-chto-takoe-adversarialattack.html and includes spoofing attacks on biometric systems, when an attacker tries to disguise himself as another person. ─ poisoning – an attacker seeks to gain access to the data and learning process of the ML model in order to disrupt the learning process. Poisoning can be thought of as malicious infection of training data. The attacker possesses information about the system (Adversarial Knowledge, AK): sources and algorithms for processing data for training, training algorithms and resulting parameters. 3
The final structure of the identification and authentication system with neural network conversion of biometric parameters into a cryptographic "private" key is shown in Fig. 4.
To assess the security of the system shall use the methodology for analyzing
information security and cybersecurity based on fuzzy gray cognitive maps, detailed in
[
Fuzzy gray cognitive map (FGCM) is a directed graph defined using a tuple of sets
[
Where C – a set of concepts, which are significant factors (graph vertices), F – a set of connections between concepts (directed arcs), and W – a set of weights of FGCM connections, which can be both positive and negative for “strengthening” and “weakening” the influence of the concept, respectively.
The use of the algebra of “gray” numbers when specifying the set W allows the use of a fuzzy linguistic scale, considering the degree of confidence of the expert in the current assessment (Table 4). The state of concepts X will also be defined as a “gray” number at an arbitrary discrete moment in time t N 0 :
FGCM = 〈C, F, W〉, (1)
Where X i t and X i t 1 – the values of the concept state variable at times t and t 1 , n – number of concepts in FGCM, f () – nonlinear concept function (hyperbolic tangent). †† Database of information security threats FSTEC, https://bdu.fstec.ru/threat, last accessed 2021/01/10.
In Table 6, the main vulnerabilities correspond to the C7 – C9 FGCM concepts. Threats C2 – C6 correspond to scenarios of exposure to an external attacker in the course of exploiting one or more system vulnerabilities. The assessment of local relative risks of violation of information security and cybersecurity of the NSBIA system was carried out for the most likely attack vectors. The corresponding FGCM is shown in Fig. 5.
Fig. 5. Fuzzy cognitive map for assessing local relative risks of information security and cybersecurity breach NSBIA.
Concept ExtAt1
C2 C3 C4 С5 С6 C7 C8 C9 C10 C11 C12 C13 C14
Let us consider the scenario of an attacker's impact with and without using a countermeasure based on a neural network transformation “biometrics-key” to ensure information security and cybersecurity of the NSBIA.
Fig. 6 andFig. 7 below show the process of changing the state of FGCM concepts in the event of an attacker without using and using the implementation of the neural network transformation “biometrics-key” to ensure information security and cybersecurity of NSBIA as a defensive countermeasure. Fig. 6. Change in time of the state of (a) “grayness” – the spread of the assessment, (b) “bleached” – the central meaning of the gray assessment) of concepts under the influence of an attacker without using the implementation of the neural network transformation “biometrics-key”.
Local relative risk indicators for target concepts C13, C14 are shown in Table 7. Violation of the system's performance and refusal to use it (breach of integrity) Modification of the base and ML model (breach of confidentiality) without the use of neural network transformation “biometrics-key” [0.0162; 0.4156] after applying the neural network transformation “biometrics-key” [0.0442; 0.2560]
The use of cognitive analysis in the task of assessing information security and cybersecurity risks allows us to consider the range of opinions of experts, as well as the inaccuracy and incompleteness of the data collected during the audit on the state and properties of the information system. Cognitive models allow one to formalize the mutual influence of system elements and the destabilizing effects of internal and external abusers who exploit vulnerabilities of software and hardware components, which are a significant decision-making tool in the process of qualitative and quantitative assessments. Scenarios for modeling the impact of an attacker using a gray fuzzy cognitive map built based on expert data make it possible to assess the effectiveness of the applied protection tools and select the optimal combination of applied solutions, considering the identified threats and potential attack vectors on NSBIA, including ML models for processing biometric data. 5
The paper proposes an approach to the analysis of the security of integrated biometric authentication and identification systems based on gray fuzzy cognitive maps. A feature of the biometric system is the use of a neural network transformation “biometrics-key”, which provides distributed storage of the base of biometric images and allows the use of a secret cryptographic key generated based on the image as an output of the neural network.
To assess the security of biometric authentication and identification systems using ML models, an analysis of current threats, vulnerabilities and potential attack vectors was carried out, on the basis of which a fuzzy gray cognitive map was built to assess local relative risks of ensuring information security and cybersecurity in the event of an attacker without using and using neural network transformation “biometrics-key”. Local relative risk indicators for key information resources decreased by 45%. 6
The reported study was funded by Ministry of Science and Higher Education of the Russian Federation (information security) as part of research project № 1/2020.