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All Journal Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
Inaam Salman Aboud
College of Education Al-Mustansiriya University, Baghdad, Iraq
Computer Science & IT Engineering Mechanical Engineering Transportation

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Password Authentication Using Modify Multi-Connect Architecture Associative Memory Rusul Hussein Hasan; Inaam Salman Aboud; Rasha Majid Hassoon
Vokasi UNESA Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 2 (2025)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v2i2.38218

Abstract

Traditional password authentication systems often face challenges related to security vulnerabilities, slow authentication speeds, and susceptibility to noise interference. While previous methods such as Multi-Bias Associative Memory (MBAM) have made progress in improving authentication performance, they still suffer from inefficiencies in training time, accuracy, and robustness to noisy input data.To address these limitations, this research introduces an Enhanced Password Authentication System leveraging Modified Multi-Connect Architecture (MMCA) associative memory, supporting both graphical and textual passwords. MMCA enhances pattern recognition, enabling faster and more accurate authentication while ensuring robustness against noise interference. Compared to traditional methods, MMCA reduces computational overhead, improves resistance to adversarial inputs, and accelerates the authentication process.Experimental validation on 100 trials demonstrates 100% authentication accuracy for both graphical and textual password-based methods. The system achieves authentication times of 0.5 seconds for textual passwords and 1 second for graphical passwords, significantly outperforming existing solutions. Additionally, MMCA maintains reliable authentication even in the presence of up to 15% noise in graphical passwords. Comparative analysis shows that MMCA surpasses MBAM and other approaches in training efficiency and authentication speed, making it a promising solution for secure, fast, and noise-resistant user authentication.
Co-Authors Rasha Majid Hassoon Rusul Hussein Hasan