An Ensemble Based Computational Social System for Fake News Detection in MANET Messaging

Abstract

Mobile Adhoc Networks (MANETs) are utilised in a variety of mission critical situations and as such it is important to detect any fake news that exists in such networks. This research combines the power of Veracity, a unique, computational social system with that of Legitimacy, a dedicated ensemble learning technique, to detect Fake News in MANET Messaging. Veracity uses five algorithms namely, VerifyNews, CompareText, PredictCred, CredScore and EyeTruth for the capture, computation and analysis of the credibility and content data features using computational social intelligence. To validate Veracity, a dataset of publisher credibility-based and message content-based features is generated to predict fake news. To analyse the data features, Legitimacy, a unique ensemble learning prediction model is used. Four analytical methodologies are used to analyse these experimental results. The analysis of the results reports a good performance of the Veracity architecture combined with the Legitimacy model for the task of fake news detection in MANET Messaging.

Adaptive Marine Predator Optimization Algorithm (AOMA)-Deep Supervised Learning Classification (DSLC) Based IDS Framework for MANET Security

“…However, the majority of systems based on these techniques struggle with high false negative and false positive detection rates as well as a lack of ongoing adaptation to malicious behaviors that change over time. Therefore, the Artificial Intelligence (AI) mechanisms [16][17][18][19] vulnerabilities and flaws. However, the conventional AI-based IDS [20,21] are unreliable and inaccurate.…”