activities for professional, personal, and financial operations. It has been pointed out that even though traditional authentication approaches, such as PINs, passwords, and static biometrics, still provide a certain security level in a system, they have been shown to be subjected to many threats, including data sensitivity leakage, phishing, theft, and brute force attacks [1]. Other static biometric methods, e.g., facial recognition and fingerprint, have enhanced and increased the security of the system; however, it has been proven that they are subject to static data constraints, environmental conditions, and spoofing-based attacks [2]. To address the aforementioned shortcomings and improve usability and security, dynamic promising solutions should be adopted [3]. Behavioral biometrics have been considered one of the best prominent solutions for system authentication issues since they analyze in-depth the personal specific interaction patterns, including swipe gestures, touch pressure, and (X, Y) coordinates of spatial movements. These approaches can repeatedly provide authentication and also be adapted to dynamic user behavior compared to traditional approaches, such as static techniques. Motion data and touch dynamics have been investigated by many prior works in order to precisely recognize unique user features; however, they are not strong enough to resist real-world variations and conditions, e.g., counterfeit attempts and environmental and climate modifications [4; 5]. Such problems ensure the real need for more resilient techniques. Interestingly, in order to further refine authentication systems, the three-dimensional (3D) touch sensors have been widely leveraged in many different applications. For instance, to get unique interaction patterns for each person, contemporary techniques have been implemented using 3D touch position and pressure sensitivity, and this significantly elevates the security level and the robustness of the system [6]. Other methodologies, e.g., audio and sound sensors, have been augmented with the 3D touch sensors to be employed for mobile healthcare in order to keep the data of the patient private and secure [7]. To significantly refine the security level of a design, the biometric data, e.g., electroencephalogram (EEG) signal processing, can be combined with sensor data [8]. While these advances contribute to improved security, there is still a need to integrate touch pressure and location data into a unified framework for more robust authentication. Previous works have not focused on integrating 3D touch pressure data with on-screen finger location data to create a unified framework using decision-making techniques, such as TOPSIS. In this work, we focus on addressing these limitations by presenting a novel smartphone authentication framework. The TOPSIS-based decision-making approach has been augmented with ML techniques to identify and rank critical and valuable features in user behavior. By leveraging only valuable features selected using the TOPSIS approach, and enhancing model resilience using ML algorithms, the proposed technique provides a secure, scalable, and user-friendly authentication system that can thwart many attacks, including cybersecurity threats. In order to refine the resiliency and robustness of the proposed technique, temporal scaling, noise insertion, and spatial perturbation approaches have been incorporated to mimic real-world user responses. This work proposes a secure and lightweight technique against serious threats and addresses current authentication dilemmas by integrating TOPSIS methodology with ML algorithms and dynamic pattern recognition. The main contributions of this paper are:  
1. A smartphone authentication application was developed on the Android platform to collect data from 30 participants, each performing 10 attempts. 2. A machine learning-based approach was developed for system authentication, where various machine learning algorithms were evaluated to identify the most effective model. 3.