Design and Analysis of a Novel Authorship Verification Framework for Hijacked Social Media Accounts Compromised by a Human

Abstract

Compromising the online social network account of a genuine user, by imitating the user’s writing trait for malicious purposes, is a standard method. Then, when it happens, the fast and accurate detection of intruders is an essential step to control the damage. In other words, an efficient authorship verification model is a binary classification for the investigation of the text, whether it is written by a genuine user or not. Herein, a novel authorship verification framework for hijacked social media accounts, compromised by a human, is proposed. Significant textual features are derived from a Twitter-based dataset. They are composed of 16124 tweets with 280 characters crawled and manually annotated with the authorship information. XGBoost algorithm is then used to highlight the significance of each textual feature in the dataset. Furthermore, the ELECTRE approach is utilized for feature selection, and the rank exponent weight method is applied for feature weighting. The reduced dataset is evaluated with many classifiers, and the achieved result of the F-score is 94.4%.

Solar irradiation forecastby deep learning architectures

“…On the other hand, the introduction of deep learning in the artificial intelligence field along with advances in computational power led computerized decision systems to spread diverge fields, such as medicine [6], agriculture [7], and [8,9]. Regarding the content of the current study, different approaches such as fuzzy model, artificial neural network, regression model, empiric model, and long short term memory (LSTM) unit network are used for solar irradiance predictions [10].…”