Enhancing Authentication Security: A Dynamic Lock System with Randomized PIN Mechanism

Traditional authentication mechanisms, such as PIN-based security systems, are widely used in ATMs, mobile devices, and security locks due to their simplicity and ease of use. However, these systems rely on fixed security parameters, such as a predefined number of incorrect attempts and a fixed lockout duration, making them highly vulnerable to brute-force attacks. Attackers can exploit this predictability by systematically attempting PIN combinations while taking advantage of consistent lockout intervals. Although biometric authentication and multi-factor authentication (MFA) have emerged as alternatives, they come with challenges such as hardware dependency, user inconvenience, and privacy concerns. To address these vulnerabilities, we propose a Dynamic Lock System that enhances PIN-based authentication by introducing randomization in PIN length, attempt limits, and lockout durations. Unlike traditional authentication systems, our approach ensures that system behavior is unpredictable, making brute-force attacks highly inefficient. The proposed system incorporates three key randomization features: randomized PIN selection, where the user is required to enter either a 4-digit or 5-digit PIN, increasing the number of possible combinations; randomized attempt limits, which allow between 3 and 7 incorrect attempts before triggering a lockout; and randomized lockout duration, where the system enforces a random lockout time between 10 to 60 seconds, preventing time-based attack optimizations. The effectiveness of the Dynamic Lock System was evaluated through brute-force attack simulations, where it was compared to traditional PIN-based authentication. Experimental results demonstrate that while traditional systems allow brute-force attacks to systematically crack a 4-digit PIN in approximately 7.6 days, our system extends this attack time to approximately 8.9 days due to its randomized security parameters. This unpredictability makes it significantly harder for attackers to estimate security behaviors, thereby reducing the efficiency of automated brute-force tools. By eliminating predictable authentication patterns, the proposed Dynamic Lock System enhances security without requiring additional hardware or complex verification steps. Future work will explore adaptive security mechanisms, machine learning-based anomaly detection, and cloud-based authentication integration to further improve security scalability. This system provides an efficient, cost-effective, and scalable authentication solution for modern cybersecurity applications, mobile security, and IoT devices.