Advancing Secure Authentication for Data Security with Dynamic Risk Assessment and Machine Learning in PUF-Based Systems |
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| © 2024 by IJCTT Journal | ||
| Volume-72 Issue-6 |
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| Year of Publication : 2024 | ||
| Authors : Priyanka Neelakrishnan | ||
| DOI : 10.14445/22312803/IJCTT-V72I6P106 | ||
How to Cite?
Priyanka Neelakrishnan, "Advancing Secure Authentication for Data Security with Dynamic Risk Assessment and Machine Learning in PUF-Based Systems," International Journal of Computer Trends and Technology, vol. 72, no. 6, pp. 36-49, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I6P106
Abstract
Phishing is considered one of the fraudulent social engineering techniques that applies deceitful tactics to commit cybercrimes. The process involves stealing users’ sensitive data, such as login credentials, credit card numbers, etc. A Physical Unclonable Function (PUF) is a physical object based on given inputs, creates solutions, and provides a physically defined Digital Fingerprint output that serves as a unique identifier. The attacker then uses the traffic to challenge the nodes in the PUFbased authentication protocol. Applying the developed theory that, in using internet-enabled devices, ensure physical security systems, such as PUF-based authentication, are installed to eliminate data leakage and harmful intrusion solves these threats. The two well-known phishing attacks in IoT are Man-in-the-Middle (MITM) and Denial of Service (DoS) attacks. Therefore, creating wireless nodes in the authentication security protocol will help control security during MITM or DoS attacks. Therefore, this research proposes exploiting the power of asymmetric encryption, which will be sent to the server side through a USB token. A robust PUF-based USB device for digital authentication token generation; a proof architecture to ensure security measures for sensitive military and intelligence applications, incorporating Dynamic Risk Assessment (DRA) models, such as Random Forest and XGBoost, into the PUF-based authentication framework has significantly enhanced its capability to discern and mitigate sophisticated phishing threats in real-time. These models leverage behavioral biometrics and user interaction patterns to dynamically adjust authentication protocols, fortifying the system’s resilience against MITM and DoS attacks in the IoT landscape. A detailed review of client-side and server-side protection through the proposed mechanism; Rigorous testing that proves that the proposed architecture is state-of-the-art and paradigm-changing for sensitive applications.
Keywords
User authentication, Data security, Cybersecurity, PUF, Client-Server, Phishing, Protocol .
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