This study delves into advanced feature selection methodologies for enhancing Android malware classification. GHR-Optimizer is introduced as an innovative feature selection approach combining Grey Wolf Optimization, Hill Climbing, and Random Forest Classifier method. The approach selects features from a hybrid dataset and is evaluated across machine learning, deep learning, and ensemble frameworks. A detailed comparative analysis is conducted, contrasting GHR-Optimizer with static and dynamic feature sets as well as traditional filter and wrapper-based methods. The implementation of the GHR method demonstrated superior performance, particularly when evaluated with diverse datasets such as KronoDroid, which achieved exceptional accuracy and balance in classification metrics. When integrated with the Random Forest classifier, the GHR-Optimizer achieves an accuracy of 98.40%. These findings underscore GHR-Optimizer’s superior performance in boosting classification accuracy and robustness, highlighting its pivotal role in advancing feature selection strategies within the domain.

Abstract Unravelling the dynamics of network vertices is pivotal, and traditional centrality measures have limitations in adapting to structural changes, directed and weighted networks, and temporal analyses. This paper introduces a ground breaking approach ‐ hitting time‐based centrality. Utilizing network matrix notations and a random walk model on a connected network , we establish a Markov chain to quantify the hitting time, hitting distance, and hitting centrality, providing a nuanced measure prioritizing central vertices. Through extensive experiments using Kendall's tau coefficient, the paper evaluates the method's correlation with actual influence in the Susceptible‐Infectious (SI) model, showcasing superior performance across diverse network sizes and structures. The hitting centrality method exhibits sensitivity to connectivity dynamics, effective incorporation of temporal dynamics, and robust handling of weighted and directed networks. Positive Kendall's tau coefficients underline the method's proficiency in prioritizing influential vertices by correlating hitting centrality values with actual infection ability. The demonstrated robustness to structural changes enhances its utility for dynamic network analysis. In conclusion, our hitting time‐based centrality approach emerges as a promising method, mitigating the shortcomings of traditional measures. By integrating information propagation speed, accommodating network dynamics, and enabling time‐dependent analyses, it offers a comprehensive tool for evaluating vertex importance and influence in complex networks.

Reversible data hiding in encrypted images (RDHEI) has gained significant popularity among security and privacy researchers as well as users, because of its features such as reversibility, embedding capacity (EC), and security. To enlarge the EC while ensuring the complete reversibility and security, we propose a bit-plane based RDHEI method based on multi-level blocking with quad-tree. The proposed method uses median edge detector (MED) as well as difference predictor to transform the original input image into a low-magnitude difference matrix. The difference matrix is then encoded by first employing a novel quad-tree based bit-plane representation strategy to exploit the intra-bit plane correlation and subsequently by inter bit-plane redundancy mitigation strategy to exploit inter bit-plane level correlation, for significantly condensing their size. Thus, a bigger room is reserved inside the cover image for embedding, so that a large amount of secret data can be hidden while ensuring the complete reversibility of the image. Experimental results validate the superiority of the proposed method over the state-of-the-art methods.

This study presents the design of high-speed inter-building connectivity in medical services by free-space optical (FSO) link with wireless backup, which will operate at gigabit rates. The two buildings in medical campus are ∼500 m apart. The FSO link normally uses 1550 nm wavelength but has a radio frequency (RF) link operating at 2.4 GHz as the back-up. In this, 99.999% availability is achieved by combining the optical laser beam with an alternate path RF solution. The laser beam of 1550 nm provides high-bandwidth connectivity in majority of the time, whereas the RF path maintains critical network connectivity during extreme weather events that are detrimental to the FSO link, such as thick fog. Optical wireless links suffer from atmospheric loss mainly because of fog, scintillation and precipitation. The authors investigate here the impact of fog, rain and snow effects and evaluate their performances for the optical propagation through the atmosphere. The wireless back-up link is used only in very dense fog conditions. Hybrid FSO/RF systems have also been examined as a means to provide high-bandwidth point-to-point links in the access network over larger distances.