Risk-based Authentication Research Articles

Multifactor Authentication System

The multifactor authentication system is a state- of-the-art solution created to improve security protocols across numerous sectors and shield confidential data from unauthorized access. Several layers of authentication are provided by this system, which makes use of cutting-edge technologies, ensuring that only people with the proper authorization can access particular resources or systems. It integrates biometric authentication, including fingerprint, facial, or iris scanning, which ups security because these biological characteristics are personal to each person. This system also uses multi-factor authentication, which combines two or more authentication factors, such as something the user is (a smart card), something they have (a password), or something they know (a password) (biometrics). This combination lessens the possibility of a single point of failure, hence enhancing security. Real-time monitoring, risk-based authentication, and behavioral biometrics are further aspects of the advanced authentication system that help to identify and stop fraudulent actions. Organizations can dramatically reduce security risks, safeguard sensitive data, and adhere to strict regulatory requirements by deploying this solution. The advancedauthentication system, in its whole, is a potent tool in the modern digital environment, protecting important assets with its strong and complex security measures. Keywords: advanced authentication system, security, biometric authentication, multi-factor authentication, real-time monitoring, risk- based authentication,

Detecting Risky Authentication Using the OpenID Connect Token Exchange Time.

With the rise in sophisticated cyber threats, traditional authentication methods are no longer sufficient. Risk-based authentication (RBA) plays a critical role in the context of the zero trust framework-a paradigm shift that assumes no trust within or outside the network. This research introduces a novel proposal as its core: utilization of the time required by OpenID Connect (OIDC) token exchanges as a new RBA feature. This innovative approach enables the detection of tunneled connections without any intervention from the user's browser or device. By analyzing the duration of OIDC token exchanges, the system can identify any irregularities that may signify unauthorized access attempts. This approach not only improves upon existing RBA frameworks but is also in alignment with the broader movement toward intelligent and responsive security systems.

RLAuth: A Risk-Based Authentication System Using Reinforcement Learning

RLAuth: A Risk-Based Authentication System Using Reinforcement Learning

Customer Authentication in Mobile Banking-MLOps Practices and AI-Driven Biometric Authentication Systems

The intersection of customer demands, security, and innovative services in the diverse mobile banking space calls for continuous adaptation by financial institutions. Small challenges such as on-demand customization and scalability are addressed through merging technologies, which is important for smaller institutions undergoing IT modernization. Despite the slow pace of ML adoption in banking, those leveraging ML experience increased their success in this competitive landscape. This article looks at the role of MLOps in overcoming challenges posed by evolving data volumes and complexities in deploying and developing ML models within financial institutions. As online banking authentication holds an important role in securing financial transactions, a historical overview of authentication methods, from biometrics to tokens, creates a chance to delve into the transformative potential of AI-driven biometric authentication. With the increase in mobile banking fraud, the need to safeguard sensitive customer data is met with radical technology. The article examines the varied authentication methods employed in online banking applications and depicts the potential of biometrics, majorly behavioral biometrics, to improve security and user experience. The rise of online mobile banking systems introduces both convenience and security concerns, thus prompting a closer look at the adoption of biometrics to mitigate fraud risks and improve the seamless authentication process throughout the user session. Customers increasingly demand quick and easy mobile payments, so biometrics has become a key fraud prevention and detection solution. By running in the background and eliminating setup authentication and risk-based authentication, behavioral biometrics significantly reduces fraud, thus addressing the limitations of traditional authentication methods like email verification and passwords. The article navigates the evolving mobile banking security space, highlighting the important role of MLOps and the potential of AI-driven biometric authentication in meeting the dual objectives of improving customer experience and strengthening severity protocols.

A study of risk-based authentication system in cyber security using machine learning

The optimum authentication method is determined by the user's risk profile, which is created using context- and behavior-based data from the user's device, finger print, one-time password, and other characteristics. Hacking and security breaches of online accounts, including social networking and web ac- counts, are very common in today's society. We suggest a Risk Based Authentication System utilizing Machine Learning to stop this. For the protection of data and money in this internet environment, security is a worry. Numerous parameters are researched and taken into consideration in the paper in order to solve the issue. These variables determine whether to grant the user permission or not. The gradients descent method is used to verify the user. Previous literature is re- viewed with technical details of the system before conclusion.

Pump Up Password Security! Evaluating and Enhancing Risk-Based Authentication on a Real-World Large-Scale Online Service

Risk-based authentication (RBA) aims to protect users against attacks involving stolen passwords. RBA monitors features during login, and requests re-authentication when feature values widely differ from those previously observed. It is recommended by various national security organizations, and users perceive it more usable than and equally secure to equivalent two-factor authentication. Despite that, RBA is still used by very few online services. Reasons for this include a lack of validated open resources on RBA properties, implementation, and configuration. This effectively hinders the RBA research, development, and adoption progress. To close this gap, we provide the first long-term RBA analysis on a real-world large-scale online service. We collected feature data of 3.3 million users and 31.3 million login attempts over more than 1 year. Based on the data, we provide (i) studies on RBA’s real-world characteristics plus its configurations and enhancements to balance usability, security, and privacy; (ii) a machine learning–based RBA parameter optimization method to support administrators finding an optimal configuration for their own use case scenario; (iii) an evaluation of the round-trip time feature’s potential to replace the IP address for enhanced user privacy; and (iv) a synthesized RBA dataset to reproduce this research and to foster future RBA research. Our results provide insights on selecting an optimized RBA configuration so that users profit from RBA after just a few logins. The open dataset enables researchers to study, test, and improve RBA for widespread deployment in the wild.

Toward a Secure and Usable User Authentication Mechanism for Mobile Passenger ID Devices for Land/Sea Border Control

Nowadays the critical sector of transport becomes progressively more dependent on digital technologies to perform essential activities and develop novel efficient transport services and infrastructure to empower economic and social cohesion exploiting the economic strengths of the European Union (EU). However, although the continuously increasing number of visitors, entering the EU through land-border crossing points or seaports, brings immense economic value, novel border control solutions, such as mobile devices for passenger identification for land/sea border control, are essential to precisely identify passengers “on the fly” ensuring their comfort. Nevertheless, these devices are expected to handle highly confidential personal data and thus, it is very likely to become an attractive target to malicious actors. Therefore, to ensure high level of device security without interrupting border control activities, strong secure and usable user authentication mechanisms are required. Towards this direction, we, firstly, discuss risk-based and adaptive authentication for mobile devices as a suitable approach to deal with the security vs. usability challenge and a novel risk-based adaptive user authentication mechanism is proposed to address this challenge. Afterwards, a set of popular Machine Learning (ML) classification algorithms for risk-based authentication was tested and evaluated on the HuMIdb (Human Mobile Interaction database) dataset to identify the most appropriate ones for the proposed mechanism. The evaluation results demonstrated impact of overfitting (i.e., accuracy: 1,0000) and therefore, we considered novelty detection algorithms to overcome this challenge and demonstrate high performance. To the best of our knowledge, this is the first time that novelty detection algorithms have been considered for risk-based adaptive user authentication showing promising results (OneClassSVM 0,9536, LOF 0,9740, KNN_average 0,9998).

Verify It’s You: How Users Perceive Risk-Based Authentication

Risk-based authentication (RBA) is an adaptive security measure used to strengthen password-based authentication against account takeover attacks. Our study on 65 participants shows that users find RBA more usable than two-factor authentication equivalents and more secure than password-only authentication.

Impact of Behavioural Biometrics on Mobile Banking System

The rise of online mobile banking systems has given customers a convenient and better way to interact with the bank as and when they want. Smartphones have revolutionized the world of payment. People now use mobile phones more often to make payments. But with increasing frauds and cybercrimes, customer experience and security become the major concern for any bank. In such situations, biometrics, especially behavioural biometrics, can be used. In this paper, we are going to discuss how behavioural biometrics can be widely adopted to reduce fraud. To increase the adoption of mobile payment acceptance, solutions need to be prioritized and reduce unnecessary obstacles. Customers demand mobile payments that are easy and quick. This is also used for fraud detection and prevention due to increased amounts of identity fraud attempts. Currently used methods for authentication such as passwords and email verification are quite problematic for security and user experience. This gives rise to behavioral biometrics. It is very important nowadays to authenticate the user throughout the session, and this is exactly what behavioral biometric do. Behavioral biometrics achieves this by invisibly running in the background and not interfering with the user experience. Fraud is significantly reduced by removing the setup authentication and risk-based authentication completely.

Open sesame: Lessons in password-based user authentication

The cost of unusable password policies in the wild is well documented. These costs impinge both business and security. The alternative is to move to multi-factor and risk-based authentication, which include software authenticators, hardware tokens, and biometrics. This paper provides an overview of the research in this area and concludes with guidance on how to best leverage password-based authentication. We recommend that designers should only implement efforts backed by empirical evidence, offer solutions to reduce user effort, and use compensating controls to address the underlying limitations of passwords.

키스트로크 다이나믹스 및 적응형 위험 기반 인증방식을 활용한 사용자 인증 시스템 구현

키스트로크 다이나믹스 및 적응형 위험 기반 인증방식을 활용한 사용자 인증 시스템 구현

A new risk-based authentication management model oriented on user's experience

With the increasing role of numerous Internet services, more and more private data must be protected. One of the mechanisms which is used to ensure data security is user authentication. A reliable authentication mechanism is a foundation of security of a remote service but, on the other hand, it is also a source of user frustration because of fear of losing access in case of three failures. A remedy to this problem could be contextual secure authentication. Such a protocol should provide multi-level authentication mechanism which increases user satisfaction without decreasing a protection level. In this paper we propose a risk analysis procedure of a new authentication management model using contextual data and oriented on user experience. We describe an approach to risk assessment of the mechanism, which supports a process of choosing the proper multi-step authentication procedure. On this basis, it is possible to provide a security solution which keeps balance between user satisfaction (related to QoE) and the obtained Level of Security (related to QoP).

Risk-based authentication: biometrics' brave new world

As digital consumers, we expect our interactions to be frictionless: we are accustomed to making low-value payments using a card or mobile device without the disruption caused by entering a complex passcode. But we also want to enjoy more security, not just in banking but across all our interactive experiences – being able to verify our identity quickly, securely and without fuss. The challenge this presents for organisations is to offer a superior customer experience without compromising security.

IJARCCE - Computer and Communication Engineering

Online and Enterprise resources typically required authentication before user allow to access the sensitive applications and information. Sensitive information generally contains user personal information, transactions, confidential data, etc. Traditional user authentication system used user identifier, Password, Personal Identification Number (PIN), Token code etc. These Systems can't fulfil the current requirement of the user authentication. So that most of the system used multilevel and multifactor authentication mechanism to allow authorized user to get access the sensitive application and information. Recently such multilevel and multifactor security is provided using Risk Based Authentication (RBA) mechanism. The RBA provides access based on Enforcement policy and access decision based on the risk score. Due to which RBA mechanism provides a more secure way to access the sensitive application and information by the user. In this paper, we will propose RBA mechanism based on User's machine specific authentication information and Dynamic Quick Response (QR) code based One Time Password generation for User step Up Authentication.

Online risk-based authentication using behavioral biometrics

In digital home networks, it is expected that independent smart devices communicate and cooperate with each other, without the knowledge of the fundamental communication technology, on the basis of a distributed operating system paradigm. In such context, securing the access rights to some objects such as data, apparatus, and contents, is still a challenge. This paper introduces a risk-based authentication technique based on behavioral biometrics as solution approach to tackle this challenge. Risk-based authentication is an increasingly popular component in the security architecture deployed by many organizations to mitigate online identity fraud. Risk-based authentication uses contextual and historical information extracted from online communications to build a risk profile for the user that can be used accordingly to make authentication and authorization decisions. Existing risk-based authentication systems rely on basic web communication information such as the source IP address or the velocity of transactions performed by a specific account, or originating from a certain IP address. Such information can easily be spoofed, and as such, put in question the robustness and reliability of the proposed systems. In this paper, we propose a new online risk-based authentication system that provides more robust user identity information by combining mouse dynamics and keystroke dynamics biometrics in a multimodal framework. We propose a Bayesian network model for analyzing free keystrokes and free mouse movements involved in web sessions. Experimental evaluation of our proposed model with 24 participants yields an Equal Error Rate of 8.21 %. This is very encouraging considering that we are dealing with free text and free mouse movements, and the fact that many web sessions tend to be very short.