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As social media platforms gain popularity and influence, content integrity and user accountability issues become more critical. Authorship attribution (AA) is a powerful tool for tackling such issues by accurately determining the real author of online posts. This study proposes an AA approach using machine and deep learning algorithms to accurately predict the author of unknown posts on social media platforms. It introduces Temporal Convolutional Networks (TCN) for short texts, investigates the effectiveness of combining Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN), and explores the use of an Autoencoder combined with Adaboost classifier. This approach was tested on a Twitter dataset, achieving 52.77% accuracy in AA through multiple experiments across various scenarios.

Smishing is a type of social engineering attack that involves sending fraudulent SMS messages to trick recipients into revealing sensitive information. In recent years, it has become a significant threat to mobile communications. In this study, we introduce a novel smishing detection method based on federated learning, which is a decentralized approach ensuring data privacy. We develop a robust detection model within a federated learning framework based on deep learning methods such as Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM). Our experiments show that the federated learning method using Bi-LSTM achieves an accuracy of 88.78%, highlighting its effectiveness in tackling smishing detection while preserving user privacy. This approach not only offers a promising solution to smishing attacks but also lays the groundwork for future research in mobile security and privacy-preserving machine learning.

In order to stop hate speech spreaders on social media, researchers started developing machine learning systems that automatically detect hate speech. In this paper we present our proposed approach to detect hate speech on twitter. We develop two different models, the first one with a traditional approach using random forest model and the second one with the autoencoder as a deep learning model combinate with random forest model. The proposed models reached accuracies of 94% and 63% for Kaggle (https://www.kaggle.com/) and Pan (https://pan.webis.de/) dataset respectively.

•This study concerns Non-Darcian Immiscible Two-Phase Flow and Heat Transfer in Porous Materials considering (Darcy-Forchheimer–Brinkman Model)•The influence of inertia “Forschheimer term”, Porosity, gravity, and macroscopic shear “Brinkman term” are pointedly analyzed numerically with finite volume scheme.•Test section of a vertical, cylinder is filled with spherical beads and exposed to constant wall temperature. The two flowing fluids are assumed to move concurrently, downward, and steady with no variation in their properties along time and length.•Numerical Solution was compared with experimental results achieving good agreement.