Enhancing UAV Security Against GPS Spoofing Attacks Through a Genetic Algorithm-Driven Deep Learning Framework

Information, Год журнала: 2025, Номер 16(2), С. 115 - 115

Опубликована: Фев. 7, 2025

Unmanned Aerial Vehicles (UAVs) are increasingly employed across various domains, including communication, military, and delivery operations. Their reliance on the Global Positioning System (GPS) renders them vulnerable to GPS spoofing attacks, in which adversaries transmit false signals manipulate UAVs’ navigation, potentially leading severe security risks. This paper presents an enhanced integration of Long Short-Term Memory (LSTM) networks with a Genetic Algorithm (GA) for detection. Although GA–neural network combinations have existed decades, our method expands GA’s search space optimize wider range hyperparameters, thereby improving adaptability dynamic operational scenarios. The framework is evaluated using real-world dataset that includes authentic malicious under multiple attack conditions. While we discuss strategies mitigating CPU resource demands computational overhead, acknowledge direct measurements energy consumption or inference latency not included present work. Experimental results show proposed LSTM–GA approach achieved notable increase classification accuracy (from 88.42% 93.12%) F1 score 87.63% 93.39%). These findings highlight system’s potential strengthen UAV against provided hardware constraints other limitations carefully managed real deployments.

Язык: Английский

K-Means Clustering for Portfolio Optimization: Symmetry in Risk–Return Tradeoff, Liquidity, Profitability, and Solvency

Symmetry, Год журнала: 2025, Номер 17(6), С. 847 - 847

Опубликована: Май 29, 2025

In order to evaluate the impact of k-means clustering on portfolio optimization, this study groups enterprises based profitability, liquidity, and solvency indicators. The confirms positive correlation between risk, return, risk-adjusted performance through an analysis historical financial records. After companies were divided into two groups, equal-weighted portfolios created using these groupings. Although they produced higher returns, cluster 1 portfolios, which included more risky companies, also showed volatility. Cluster 0 other hand, offered less risk consistent results. Portfolios clustered by ROA, OCFM, GPM outperformed market benchmark highest returns adjusted for according Sharpe Ratio analysis. Furthermore, emphasizes that although liquidity metrics play a role in selection, increased does not always translate improved performance. terms methodology, Silhouette Analysis Elbow technique determining optimal number clusters. All things considered, results show how data-driven techniques may be used align strategies investors’ tolerances.

Язык: Английский