GANTouch: An Attack-Resilient Framework for Touch-Based Continuous Authentication System

Agrawal, Mohit; Mehrotra, Pragyan; Kumar, Rajesh; Shah, Rajiv Ratn

doi:10.1109/tbiom.2022.3206321

Cited by 7 publications

(2 citation statements)

References 34 publications

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“…They have been tested and evaluated for usability and security, and they demonstrate good resistance to attacks and high detection accuracy. The vulnerability of touch-based continuous authentication systems (TCAS) to active adversarial attacks is investigated in [35]. The significant role of security incidents caused by insiders in attacks against organizations is addressed in [36], which proposes a continuous authentication solution for large multisite enterprises based on Apache Spark, Apache Cassandra, Kafka, and a MySQL database.…”

Section: Related Workmentioning

confidence: 99%

Leveraging Machine Learning for Wi-Fi-Based Environmental Continuous Two-Factor Authentication

AlQahtani,

Alshayeb,

Nabil

et al. 2024

IEEE Access

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The traditional two-factor authentication (2FA) methods primarily rely on the user manually entering a code or token during the authentication process. This can be burdensome and time-consuming, particularly for users who must be authenticated frequently. To tackle this challenge, we present a novel 2FA approach replacing the user's input with decisions made by Machine Learning (ML) that continuously verifies the user's identity with zero effort. Our system exploits unique environmental features associated with the user, such as beacon frame characteristics and Received Signal Strength Indicator (RSSI) values from Wi-Fi Access Points (APs). These features are gathered and analyzed in real-time by our ML algorithm to ascertain the user's identity. For enhanced security, our system mandates that the user's two devices (i.e., a login device and a mobile device) be situated within a predetermined proximity before granting access. This precaution ensures that unauthorized users cannot access sensitive information or systems, even with the correct login credentials. Through experimentation, we have demonstrated our system's effectiveness in determining the location of the user's devices based on beacon frame characteristics and RSSI values, achieving an accuracy of 92.4%. Additionally, we conducted comprehensive security analysis experiments to evaluate the proposed 2FA system's resilience against various cyberattacks. Our findings indicate that the system exhibits robustness and reliability in the face of these threats. The scalability, flexibility, and adaptability of our system render it a promising option for organizations and users seeking a secure and convenient authentication system.INDEX TERMS two-factor authentication, machine learning, zero effort, continuous authentication, beacon frames, wireless access points, authentication module, Wi-Fi radio waves, RSSI values, 2FA, ML.

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Section: Related Workmentioning

confidence: 99%

Leveraging Machine Learning for Wi-Fi-Based Environmental Continuous Two-Factor Authentication

AlQahtani,

Alshayeb,

Nabil

et al. 2024

IEEE Access

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“…However, that would mean everyone needs to have multiple devices. On the other hand, we aim to investigate if the use of deep generative models for generating data and using the same retrain the model would help classifiers sketch a more robust boundary between the genuine and impostor samples [1,2,39,69].…”

Section: Possible Countermeasuresmentioning

confidence: 99%

Dictionary Attack on IMU-based Gait Authentication

Kumar,

Isik,

Mohan

2023

Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security

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We present a novel adversarial model for authentication systems that use gait patterns recorded by the inertial measurement unit (IMU) built into smartphones. The attack idea is inspired by and named after the concept of a dictionary attack on knowledge (PIN or password) based authentication systems. In particular, this work investigates whether it is possible to build a dictionary of IMUGait patterns and use it to launch an attack or find an imitator who can actively reproduce IMUGait patterns that match the target's IMUGait pattern. Nine physically and demographically diverse individuals walked at various levels of four predefined controllable and adaptable gait factors (speed, step length, step width, and thighlift), producing 178 unique IMUGait patterns. Each pattern attacked a wide variety of user authentication models. The deeper analysis of error rates (before and after the attack) challenges the belief that authentication systems based on IMUGait patterns are the most difficult to spoof; further research is needed on adversarial models and associated countermeasures. CCS CONCEPTS• Security and privacy → Biometrics.

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Analysis of Attacks on Continuous Authentication Methods and Ways of Defending Against Them

Matiushin,

Korkhov,

Busko

2024

Lecture Notes in Computer Science

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GANTouch: An Attack-Resilient Framework for Touch-Based Continuous Authentication System

Cited by 7 publications

References 34 publications

Leveraging Machine Learning for Wi-Fi-Based Environmental Continuous Two-Factor Authentication

Leveraging Machine Learning for Wi-Fi-Based Environmental Continuous Two-Factor Authentication

Dictionary Attack on IMU-based Gait Authentication

Analysis of Attacks on Continuous Authentication Methods and Ways of Defending Against Them

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