Voice-based Dynamic Time Warping Recognition Scheme for Enhanced Database Access Security

Paul Avweresuo Onoma 1,*, Eferhire Valentin Ugbotu 2,, Tabitha Chukwudi Aghaunor 3,, Joy Agboi 4,, Arnold Adimabua Ojugo 5,, Christopher Chukwufunaya Odiakaose 6,, Asuobite ThankGod Max-Egba 7,,

Star Umiyemeromesu Niemogha 8, Amaka Patience Binitie 9,, and Mustapha Barau Abdullahi 10,

1,5,7,8,10 Department of Computer Science, Federal University of Petroleum Resources Effurun, Nigeria

2 Department of Data Science, University of Salford, United Kingdom 

3 Department of Data Intelligence and Technology, Robert Morris University, Pittsburg, Pennsylvania, USA

4 Department of Computer Science, Delta State University Abraka, Nigeria

6 Department of Computer, Dennis Osadebay University Asaba, Nigeria

9 Department of Computer Science, Federal College of Education (Technical) Asaba, Nigeria

Email: 1 kenbridge14@gmail.com, 2 eferhire.ugbotu@gmail.com, 3 tabitha.aghaunot@gmail.com, 4 agboijoy0@gmail.com,

5 ojugo.arnold@fupre.edu.ng, 6 osegalaxy@gmail.com, 7 max-egbaasuobite@fupre.edu.ng, 8 niemogha.star@fupre.edu.ng,

9 amaka.binitie@fcetasaba.edu.ng, 10 abdullahi.mustapha@fupre.edu.ng 

*Corresponding Author

Abstract—Rapid transformation with database security has remained imperative as unauthorized access exposes sensitive data to adversaries. To curb this, we suggest using a secured dynamic time-warp scheme to improve access to the database schemas. The study integrates voice biometrics with two-factor authentication to yield a robust, user-friendly platform, which utilizes time-warping to authenticate voice patterns against the variability in utterance speed. Results showcase high accuracy and resiliency in its usage against spoofing attacks as compared to state-of-the-art voice recognition systems. The model ensures the minimal possibility of credential theft by binding the access of databases to the voice features of authorized users. The study shows the system's architecture, implementation, and performance evaluation, highlighting its potential to revolutionize database security in various applications. The findings underscore the importance of leveraging advanced biometric techniques to safeguard critical information systems.

Keywords—Time-warping; Voice-based Biometrics; Convolution Neural Network

1. Introduction

The database houses data in a tabular structure that yields efficient storage with eased retrieval and management of data [1]. The tabular mode for the relational model has been widely used to organize and access data in various industries [2]. Distributed databases allow us to replicate and share data across networks – enhancing access and its availability [3][4]. Conventional username and password-based database login is susceptible as the passwords can be corrupted or stolen, posing heavy security and integrity threats to the confidential information stored [5][6]. This requires a better login process, having a more robust and reliable authentication method [7]. With key benefits such as improved security, model accuracy, improved usability, enhanced user experience, and scalability [8][9] – we propose a secure database fused with voice recognition via dynamic time warping as an authentication process [10][11]. Previous studies on database security weaknesses, but did not propose a complete data integrity and security model [12][13]. Rather, they proposed an authentication scheme for the relational database via a one-time password (OTP) [14]; Nevertheless, it still left scope for exploitable and compromisable vulnerability in front of malicious users [15][16].

Knowledge-based authentication is an essential element of identity verification methods and solutions [17]. KBA is used in various ways – making them an efficient authentication mode [18][19]. KBA factoids are established on the knowledge that the user is alone required to possess (i.e. personal identification number, username, etc). Two commonly used authentications are [20][21]: (a) static KBA which has pre-defined questions with shared data amongst users [22]. A typical static factoid question is "What is the mother's maiden name?" or "What is the date of birth?" This technique is used by banks, and enterprises for user authentication [23]–[26].

Instant KBA dynamically generates a set of personalized questions and answers to enable user authentication. It does not require users to provide questions and answers in advance for users [27][28]. These challenge questions are dynamic and contain the correct and incorrect answers placed randomly by using data made available to the KBA system. Both of these need an advance registration against a previously existing database for credentialing [29][30]. However, KBA needs online or remote server access for checking the factoids or credentials during login. Aghware et al. [31] used a fusion-based multimodal system with face and voice biometrics. Their technique is feature-level, match score-level, rank-level, and decision-level fusion. They explored Log Gabor and LBP for face feature extraction and MFCC and LPC for voice feature extraction. It also touches on the number of fusion layers and investigates the constraints affected by various methods at the extraction and recognition level [32]–[34]. Otorokpo et al. [35] proposed face and voice biometric fusion for verification of users. Its combined score level is applied to both face/voice gait feats to bypass the limitations of the unimodal system. Setiadi et al. [36] used a multimodal biometric identification system based on voice and face recognition. The study assessed the scheme’s feasibility via statistical coefficients for voice-and-face extraction techniques such as Eigenface and Principal Component Analysis [37][38].

Previously used classifiers include Gaussian mixture [39], Neural Net [40], etc – all of which yield great results. Pande and Khampe [41] studied an android-based multimodal biometric authentication that explored both face and voice to yield a stronger face gait extraction local binary pattern with voice activity detection. It demonstrated a high accuracy of 98% for face and 89% for voice on Android-based smart terminals. Other studies have showcased significant progress in biometric modalities such as face recognition [42], voice recognition [43], vein patterns [44], and ultrasound images [45]. A holistic multimodal system can seamlessly integrate multi-biometric modalities, and enhance the expected cum requisite authentication accuracy and robustness [46].

1. Machine Learning Approaches

Many machine learning (ML) approaches/schemes are successfully implemented as Logistic Regression [47], Deep Learning [48], Adaboost [49], Naive Bayes [50], SVM [51], Random Forest [52], and others [53] as it has been effectively used to detect credit card fraud. Many of these MLs as mentioned, have their drawbacks, especially with their flexibility in feature selection/extraction approach (as either filter or wrapper) in the quest for ground truth, selected feature importance in its capability to yield faster model construction and training, and model's fitness in place of its performance accuracy. As in Table 1, a variety of ML approach contributions are as thus [54]:

1. Related Literatures Contributions

2. Study Motivation and Rationale

Gaps in previous studies include [59]–[62]:

1. Imbalanced Dataset: A major challenge with available datasets for training is that most models use the major class [63], and often ignore the minor class since they have been found to lag. We must harness the prowess of models that are explicitly tailored to mitigate the issues with imbalanced datasets [64][65].
2. Cross-Platform: With an increase in the number of OS available [66]–[68] – today’s systems must integrate cross-platform data to enhance the requisite accuracy and performance accuracy.

To address these, we propose a voice recognition system that utilizes Dynamic Time Warp (DTW) as a component of a multimodal authentication system. The study contributes to the use of a comprehensive, secure biometric authentication via the utilization of DTW, a specialized technique for aligning and comparing time series data, which holds promise in enhancing the accuracy and reliability of voice-based authentication within a multimodal context [69][70].

2. Materials and Methods

1. Overview of the Existing System

Resource security often relied on traditional password-based authentication. Users authenticate their devices by inputting a password they had either chosen or been assigned [71]. A major vulnerability is in complex passwords that are susceptible to brute-force or dictionary attacks. Users reuse passwords on multiple accounts – and it poses a significant risk [72][73]. With one account compromised, other accounts become vulnerable [74]. Thus, passwords lean towards user-friendliness, potentially compromising security. Users may forget passwords, leading to recovery and reset challenges that can affect user experience. To address these issues, [75] used an enhanced security system that builds upon traditional authentication schemes using OTPs. This has several merits: (a) OTPs are dynamically generated [76], (b) reduce risk of unauthorized access from stolen passwords [77], (c) can be used with OS login as added security prior OTP generation, (d) are time-sensitive and valid only for a short burst [78] to mitigate their risk of reuse [79], and (e) often yields complexity that makes them more challenging for the adversary to crack the digital credentials [80][81]. Thus, Fig. 1 shows security architecture with privileges.

1. The architecture of database security

The existing system is made of four modules:

1. The authentication server generates OTP – and checks for username and password, and OS user authentication. Having inputted the correct OTP, a user is then connected to the database,
2. Availability: data is uploaded on a database server with access provided to the user after the system validates user input OTP. The database makes available user privileges provided by the database admin to read, run, or update the data,
3. Access: The Admin registers users and provides them with the requisite login username and password. Registration of user(s) is performed by Admin [82][83],
4. Integrity: The data that is created or modified by the user is governed by a set of pre-defined rules. These rules are defined by the application administrator or database developer [84]–[86].

1. The Proposed System Architecture

The proposed system explores a voice recognition scheme as an authentication layer for users. Fig. 2 shows parts of secure authentication by its functionality integrating the Dynamic Time Warp (DTW) algorithm.

1. Acoustic Front-End serves thus: (a) transforms the speech signal as recorded via the microphone, into suitable features. The features are needed for voice recognition [87][88]. In this process of feature extraction, the DTW algorithm can be utilized, and (b) helps match the input audio waveform (recorded voice at the time of login) and an enrolled voice template, and both sequences lining up considering differences in timing and speed, which is of excellent utility in voice recognition [89].
2. The Acoustic Model does the following: (a) it estimates the word or phone model parameters from the acoustic vectors obtained from the training data. In the process, DTW can also be applied in training to map reference voice patterns against the recognized command or text, and (b) DTW alignment during training is used to produce accurate acoustic models that can be used for identifying words or phrases in the login process [90].
3. Lexicon does the following: (a) maintains a lexicon of words or phrases which can be identified by the system. It acts as a reference for acoustic patterns to be converted into linguistic units, and (b) assists in mapping acoustic patterns to the entries of the lexicon to allow spoken words or phrases to be recognized.
4. Language Model accomplishes: (a) it defines the word sequence probability in a specific language [91][92], which allows the system to compute the probability of each word sequence, and (b) DTW is oriented towards acoustic alignment, it enhances the language model by ensuring that the accepted words or phrases spoken are highly correlated with the expected acoustic patterns.
5. Decoder performs the following functions: (a) examines all the word-sequence sets to determine the most likely word-sequence that is likely to have generated the input speech signal, and (b) DTW can be applied in this decoding procedure to compute the similarity between the captured voice and registered voice templates and assess the likelihood of a match.

Fig. 3 describes a working structure of the complete model for database access security as proposed.

2. System architecture and design of the DTW model

3. High-level schematics for the proposed DWT voice-based recognition framework

Performance evaluation is pivotal/crucial to validate its effectiveness as hinged on 3 key metrics of accuracy, precision, and sensitivity [93] – to proffer insights into the system's capabilities. Computations make use of historical data that was not encountered during system training, ensuring the system's reliability and accuracy in securing database access. The app domains highlight the versatility and broad relevance of the proposed database security solution that includes dynamic time-warping voice recognition [94]. Implementing the system will enhance security, protect sensitive data, and ensure robust user authentication while also adapting to the specific requirements of each industry.

3. Result Findings and Discussion

1. System Design Interface

The system explores two (2) distinct sessions, namely: (a) user session – which starts with a user access of a default application installed. The activation of the program app is initialized by clicking the displayed icon on the desktop – from which the launched program presents the user with a voice-login page. It acts as a gateway to authenticate the user via their voice (signal) pattern using the API that consists of a variety of active buttons such as start voice authentication, exit, signup, and alternative login button(s) as illustrated in Fig. 4.

4. Voice login interface

Sequel to the successful registration of user details, a user can then proceed with voice enrollment to register their voice (unique) signal for which the system provides the needed flexibility via command keywords such as "Login," "Open," "Close," etc – for access to the system [95][96]. Upon completion of the registration process, user accounts undergo verification by the administrator. This crucial step ensures the integrity and security of the system before granting users access to their respective dashboards [97]–[99] – as in Fig. 5, which displays the voice enrollment to enable users' interaction with the application.

A successful voice enrolment process will then redirect a user to the login page as in Fig. 6 – whereas, Fig. 7 functions as the pivotal page that requests input of a user's details (i.e. username and password) to access the system. In addition, the pivotal page acts also as a secure gateway to help validate user credentials for registered accounts in the new system. A correctly inputted user credential will authenticate a valid user’s identity, granting access to the array of resources available. Conversely, a denied user can utilize the voice authentication page to log in to the platform cum app. Utilization of this mode often prompts the users also to compile a series of whitelisted (acceptable) keywords that were engaged during the user voice enrollment process. Upon the successful voice-verified mode for user credentials, the user is granted secure access to their personalized account, to ensure an intuitive, seamless user experience.

5. Voice enrolment interface

6. Alternate login interface

7. User dashboard

Fig. 7 shows a user dashboard to establish interaction between the user and a plethora of the system's diverse functionalities [100][101]. Its user-centric and friendly design provisions an intuitive interface that emboldens a user’s seamless navigation across the various regions of the system vis-à-vis render access to crucial data [102][103], helps the user to enact administrative roles, reprise user account settings – amongst others. It thus acts as a dynamic control pane that empowers its validated users to effectively harness its capabilities, and ensure an efficient coordination of diverse tasks. Where the user wishes to access critical data, customize settings/preferences for the user, and initialize other system actions [104], this dashboard acts as the navigation pane to optimize user productivity and experience [105][106].

The admin session helps an Admin to navigate the login page to exert control policies and preferences within the app or system as in Fig. 8. The Admin login page presents a familiar interface, prompting an Admin to provide their designated username and password. Functioning as the gateway to administrative functionalities, the admin login page serves as a crucial checkpoint for verifying the administrator's identity and authorization. By accurately entering their login credentials, the administrator validates their entitlement to wield administrative control, thus gaining access to a comprehensive suite of system management tools. This rigorous login process underscores the system's commitment to security, ensuring that only duly authorized individuals with administrative privileges can oversee and govern the system's operations with precision and authority [107].

8. Admin login

Successfully authenticated admin redirects such a user to the admin dashboard as in Fig. 9, which furnishes an admin with a table view of all users' logs captured in the system [108]–[110]. It renders a host of buttons that serve specific administrative needs and tasks. The suspend button deactivates user accounts (where necessary), temporarily; while the verification button activates user accounts for integration into the system [111]. The delete-account button permanently removes a user account, while, the logout button allows the admin to terminate a (group of) user’s session. 

9. Admin dashboard

This pane acts also as a nexus for admin tasks as well as provision tools for swift actions as well as insight cum monitoring of user activities. Thus, it equips the admin with robust functionalities via its intuitive API that enables the admin to efficiently oversee all system operations, monitor user activities/interactions, and execute administrative tasks with precision and efficiency [112].

4. Conclusion

The proposed platform uses a conventional username and password authentication method, which is vulnerable to risk, and demands an innovative approach. We then integrated the dynamic time-warp voice recognition to enhance security and user experience for sensitive data access. The literature revealed numerous pitfalls with conventional authentication mode. Thus, the proposed system draws from earlier work carried out on one-time passwords, incorporating best practices to enhance both security and dependability. The system yields a framework with enhanced usability and robust security. Its performance validated its efficiency with well-documented training to ensure that administrators and end-users can effectively manage the system. Our changeover procedure offers a clear transition path from an existing-to-new, secure database model aimed at minimizing disruptions, while preserving data integrity [104].

This study presents an innovative, comprehensive solution to the pressing issue of database security. It not only addresses the limitations of traditional authentication methods but also provides a systematic approach to system development, implementation, and ongoing maintenance. By emphasizing the importance of user training, documentation, and changeover procedures, it ensures that the proposed system can be effectively integrated into existing infrastructure, delivering an elevated level of security and user authentication. The project represents a significant step forward in the ongoing effort to safeguard sensitive data [104] and ensure the integrity of databases.

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