International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 11 | Nov 2025
p-ISSN: 2395-0072
www.irjet.net
KeyAuth: Keystroke dynamics authentication with stealth surveillance and honeypot UI for continuous protection of digital assets. Hemalatha G1, Anandha Kumar V2, Chandru K3, Chennakesavan M4, Hariharan R S5 1Assistant Professor, Dept. of Cyber Security Engineering, Paavai Engineering College, Tamilnadu, India 2Student, Dept. of Cyber Security Engineering, Paavai Engineering College, Tamilnadu, India 3Student, Dept. of Cyber Security Engineering, Paavai Engineering College, Tamilnadu, India 4Student, Dept. of Cyber Security Engineering, Paavai Engineering College, Tamilnadu, India 5Student, Dept. of Cyber Security Engineering, Paavai Engineering College, Tamilnadu, India
---------------------------------------------------------------------***--------------------------------------------------------------------analysis with a One-Class Support Vector Machine (SVM) Abstract - The rapid growth of digital services has made
model. If this check fails, the system activates a secondary facial recognition layer, employing the Deep Face framework. If both layers fail, a honeypot environment is set up to engage and monitor intruders while keeping real user data safe. The KeyAuth framework is built using Flask (Python) for backend functions, including machine learning and facial recognition. The frontend is created with HTML, CSS, and JavaScript for an interactive user experience. This combined method ensures a flexible, precise, and user-friendly security system that meets the needs of modern web applications.
traditional password-based authentication very vulnerable to security breaches. This project introduces KeyAuth, a layered biometric authentication system that combines keystroke dynamics, facial recognition, and honeypot deception to improve user verification. During registration, the system records each user’s typing rhythm and facial image. It trains a One-Class SVM model using the extracted keystroke timing features. During login, authentication first checks the validity of the password and the user’s keystroke pattern. If there are mismatches, a secondary facial verification layer using DeepFace is triggered. If both layers fail, a honeypot interface activates to catch potential attackers. The system uses Flask (Python) for backend machine learning operations and HTML/CSS/JS for the user interface. Experimental evaluation shows that the model effectively tells genuine users apart from impostors, providing better protection against spoofing and credential theft compared to regular methods.
1.1 Background of the Study With the growth of artificial intelligence and data-driven applications, cyber threats have become more sophisticated. Attackers take advantage of weaknesses in static login systems, which can lead to identity theft, data breaches, and unauthorized access to sensitive information. Biometric authentication, based on unique human traits, offers better security but often requires special hardware and higher costs for implementation.
Key Words: Multi-factor authentication, Keystroke
dynamics, Facial recognition, One-Class SVM, DeepFace, Honeypot security.
Keystroke dynamics, on the other hand, provides a software-based biometric solution by analyzing typing rhythm patterns that are unique to each person. When combined with facial recognition, it forms a hybrid model that captures both behavioral and physical traits, significantly improving system strength. KeyAuth uses these features together to maintain ongoing and layered authentication without compromising usability.
1.INTRODUCTION In today's digital world, authentication is essential for secure access control in online systems. As web applications, online transactions, and cloud services continue to grow, protecting user identity has become more important. Traditional password-based authentication methods are widely used but vulnerable to many attacks, including phishing, brute force, and credential stuffing. Users often reuse or choose weak passwords, which further weaken system security. Therefore, we need intelligent, multi-layered authentication methods that offer better reliability and protection against unauthorized access. To tackle these issues, this paper introduces Key Author, a three-layer authentication system that uses keystroke dynamics, facial recognition, and a honeypot deception method. This system combines behavioral and biometric verification to improve both security and precision. First, it verifies user identity through password matching and keystroke
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1.2 Objective of the Project The main goal of the proposed KeyAuth system is to create and implement a multi-layer authentication framework that improves security and verifies user identities. The project combines behavioral and biometric authentication methods to reduce unauthorized access and strengthen login systems. It uses keystroke dynamics as a behavioral biometric feature by examining each user's unique typing rhythm through a One-Class SVM (Support Vector Machine) model. Additionally, facial recognition with the DeepFace framework acts as a secondary
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