With the increasing popularity of smartphones, user identification has become a critical component to ensure security and privacy. This study looked into how smartphone sensors’ data can be utilized to identify/authenticate users and gives suggestions for the best application components. A public smartphone dataset was used to train a deep learning algorithms, conventional classifiers, and voting classifiers, which were then used to identify the users. Feature selection and Pre-processing techniques were investigated to improve the performance. According to the results, Recursive Feature Elimination beat the other feature-selection approaches, and Long Short-Term Memory (LSTM) had the best identification performance, as evidenced by a relatively large number of machine learning performance metrics. Even with a larger number of users, the proposed identification system performed well and outperformed existing approaches, which were primarily designed and tested on the same public smartphone dataset. In terms of user authentication, this study compared the effectiveness of accelerometer data against gyroscope data. According to the findings, the accelerometer data surpassed the gyroscope data in the authentication process. Notably, the study revealed that employing LSTM to combine the accelerometer and gyroscope data resulted in near-perfect user authentication. The insights gained from this study help to develop user identification and authentication approaches that employ smartphone accelerometer data.

As wireless communication technologies advance, protecting networks from emerging threats becomes increasingly important. This paper focuses on deauthentication detection and recovery in the context of WPA3 SAE (WIFI Protected Access 3 - Simultaneous Authentication of Equals), a widely used protocol for securing wireless networks. Deauthentication attacks, which forcibly disconnect legitimate users, seriously threaten the integrity and availability of network services. The primary contributions of this paper center on developing a robust detection method for distinguishing between normal network behavior and deauthentication attack activities. also delves into the design and implementation of proactive recovery mechanisms that allow for the rapid restoration of network services following a deauthentication attack. The observed results indicate that there is a significant and immediate impact on network functionality during an active authentication attack. One noticeable consequence is preventing new customers from successfully joining the network. Another consequence is that the increased number of EAPOL frames is the result of unresponsive client behavior. And the AP's attempt to complete the handshake.

The k-nearest neighbor (KNN) algorithm remains one of the most fundamental and widely-used methods in machine learning. A common rule of thumb sets the number of neighbors as k = √n, where n is the size of the training set. Despite its widespread adoption, the theoretical justification for this choice has remained obscure. We provide a comprehensive rate-based analysis. First, we derive the minimax-optimal exponent β★ = 4/(d+4) for k = n^β under standard Hölder-smoothness assumptions, recovering as a special case Theorem 1: k = √n is minimax-optimal if and only if the feature space has dimensionality d = 4. Second, Theorem 2 quantifies the sub-optimality of any fixed β as R_n(n^β) = Θ(n^(−r(β,d))) with r(β,d) = min{β, 4(1−β)/d}, yielding an asymmetric penalty for the classical rule when d ≠ 4 that we make precise in Corollary 1. The predicted rate is empirically verified across d ∈ {2,…,20} on controlled synthetic data. On 48 datasets from the OpenML-CC18 benchmark suite, the dimension-aware rule k = ⌊n^(4/(d+4))⌋ outperforms the classical √n rule in 32 of 48 head-to-head comparisons (paired Wilcoxon p = 4.6 × 10⁻⁴, mean accuracy gain +2.5 percentage points), demonstrating that the theoretical improvement translates to a practical one. We further test the Hassanat distance metric against Euclidean across all KNN variants on the same 48 datasets, finding that Hassanat outperforms Euclidean in five of six configurations (paired Wilcoxon p < 0.05), with the largest gains on unstandardized data. Cross-validation remains the strongest k-selection strategy when computationally feasible, and the theoretical results provide a principled non-cross-validated alternative.

With the growing popularity of smartphones, user identification has become an essential component of maintaining security and privacy. This study investigates how smartphone accelerometer data can be used to identify users, and it makes recommendations for the ideal application parts. Accelerometer data from the HMOG public dataset was used to train deep learning, conventional classifiers, and voting classifiers, which were then utilized to identify users. To enhance performance, feature selection and pre-processing techniques were researched. The results show that RFE feature selection outperforms other approaches and that LSTM followed by XGBoost has the best identification performance as indicated by a relatively large number of machine learning performance measures. The proposed identification system nevertheless performed well and outperformed existing methods, which were principally created and tested on the same HMOG public smartphone dataset, even with a larger number of users. Further work would be necessary for such an application to reach its full potential, though.

The k-nearest neighbor (KNN) algorithm remains one of the most fundamental and widely-used methods in machine learning. A common rule of thumb sets the number of neighbors as $k=\sqrt{n}$, where n is the size of the training set. Despite its widespread adoption, the theoretical justification for this choice has remained obscure. We provide a comprehensive rate-based analysis. First, we derive the minimax-optimal exponent ${\beta }^{*}=4/(d+4)$ for $k=n^{\beta }$ under standard Hölder-smoothness assumptions, recovering as a special case Theorem 1: under these second-order Hölder-smoothness assumptions, $k=\sqrt{n}$ attains the asymptotic minimax-optimal exponent if and only if the feature space has dimensionality $d=4$; this concerns the asymptotic rate exponent, not the finite-sample optimal value of k. Second, Theorem 2 quantifies the sub-optimality of any fixed $\beta$ as $R_n\left(n^{\beta }\right)=\mathrm{\Theta }\left(n^{-r(\beta ,d)}\right)$ with $r(\beta ,d)=min\{\beta ,4(1-\beta )/d\}$, yielding an asymmetric penalty for the classical rule when $d\ne 4$ that we make precise in Corollary 1. The predicted rate is empirically verified across $d\in \{2,\dots ,20\}$ on controlled synthetic data. On 48 datasets from the OpenML-CC18 benchmark suite, the dimension-aware rule $k=\lfloor n^{4/(d+4)}\rfloor$ outperforms the classical $\sqrt{n}$ rule in 32 of 48 head-to-head comparisons (paired Wilcoxon $p=4.6\times {10}^{-4}$, mean accuracy gain $+2.5$ percentage points), demonstrating that the theoretical improvement translates to a practical one. We further test the Hassanat distance metric against Euclidean across all KNN variants on the same 48 datasets, finding that Hassanat outperforms Euclidean in five of six configurations (paired Wilcoxon $p<0.05$), with the largest gains on unstandardized data. Cross-validation remains the strongest k-selection strategy when computationally feasible, and the theoretical results provide a principled non-cross-validated alternative.