## 2 Literature Review
### ML Techniques in Authentication Systems and Decision-Making Methods  
ML algorithms, such as Support Vector Machines (SVM), Decision Trees, and Neural Networks have provided promising solutions for improving the system performance and identifying user-specific patterns [29]. Other ML algorithms, such as Random Forest (RF) and Gradient Boosting Machine (GBM) based multi-classifiers, further improve both the generalization and the performance by decreasing overfitting [30]. ML techniques have shown exceptional performance in processing complicated behavioral data, and this allows to effectively classify user interactions. For example, both RF and SVM algorithms have been leveraged to analyze touch dynamics data and achieved high accuracy for user authentication [31; 32; 33]. [PERSON] et al. merged RF and SVM classifiers to process touch behavior datasets and obtained accuracy exceeding 85% [31]. However, in real-time performance for a flawless user experience, the computational complexity of such a technique with a high protection level still poses a challenge [33]. To clearly position the proposed method in relation to existing work, Table 1 summarizes a set of recent behavioral biometric authentication approaches. The comparison includes dataset type, applied algorithms, reported accuracy, benefits, and limitations. This enables a direct contrast of our method's novelty and performance.  
As shown in Table 1, while several previous works explored mobile behavioral biometrics, few integrated multi-criteria decision-making into the feature engineering pipeline. Our proposed method demonstrates competitive accuracy while also addressing interpretability, perturbation resilience, and feasibility for real-time use, filling a key gap identified in prior literature. To further reduce the computational penalty and complexity in real-time performance, ML models can be incorporated with decision-making techniques, such as TOPSIS, to select only valuable features [34; 35]. The hybrid ML and decision-making frameworks can help address existing issues in authentication systems. The TOPSIS and Analytic Hierarchy Process (AHP) techniques are examples of Multi-Criteria Decision-Making (MCDM) approaches that can be employed to prioritize and rank input features in many different applications. Specifically, TOPSIS identifies the highest impact parameters on a system to provide best possible solutions [34; 35], and this can help to successfully rank features in many ML models [34]. However, integrating TOPSIS and ML with behavioral biometrics for smartphone authentication is still unrevealed in the cybersecurity field. Recent deep learning approaches, particularly those leveraging convolutional and recurrent neural networks (CNNs and RNNs), have demonstrated notable success in behavioral biometrics. [PERSON] et al. (2020) [36] proposed a hybrid framework that enhances fingerprint authentication by integrating behavioral traits to defeat puppet attacks. Their method utilizes deep learning to extract subtle user-specific behaviors in combination with liveness detection. While such deep learning models offer high accuracy, they often require large datasets and high computational resources, which limits their feasibility for real-time mobile authentication scenarios. In contrast, our proposed framework adopts a lightweight architecture combining TOPSIS-based feature selection with classical ensemble models (RF, GBM, and KNN), ensuring interpretability and efficiency while maintaining high accuracy in touch-based behavioral authentication.