AI-Based Anomaly and Data Posing Classification in Mobile Crowd Sensing

Nowadays, Mobile Crowd Sensing (MCS) became the popular paradigm for sensing data. MCS is vulnerable to many types of threats and faces many challenges. Trustworthiness is one of the main MCS challenges; attackers aim to inject faulty data to corrupt the system or waste its resources. Thus, MCS organizers must ensure that no malicious users are contributing to have trusted sensed data. Faulty sensor readings in MCS can be due to sensor failure or malicious behavior. Attackers targets degrade the system performance and reduce the worker's reputation by injecting false data. This paper evaluates different machine learning algorithms classifying the received sensed data as true, a faulty sensor, or attacker behavior. These algorithms are Decision Tree (DT), Support Vector Machine (SVM), and Random Frost (RF). Evaluating the result for comparison obtained based on accuracy, precision, Recall, f1 score, and the confusion matrix. The result shows that among all classifiers, RF shows the highest accuracy of 97.9%.