Biblio

Short messages usage has been tremendously increased such as SMS, tweets and status updates. Due to its popularity and ease of use, many companies use it for advertisement purpose. Hackers also use SMS to defraud users and steal personal information. In this paper, the use of Graphs centrality metrics is proposed for spam SMS detection. The graph centrality measures: degree, closeness, and eccentricity are used for classification of SMS. Graphs for each class are created using labeled SMS and then unlabeled SMS is classified using the centrality scores of the token available in the unclassified SMS. Our results show that highest precision and recall is achieved by using degree centrality. Degree centrality achieved the highest precision i.e. 0.81 and recall i.e., 0.76 for spam messages.

We propose a crypto-aided Bayesian detection framework for detecting false data in short messages with low overhead. The proposed approach employs the Bayesian detection at the physical layer in parallel with a lightweight cryptographic detection, followed by combining the two detection outcomes. We develop the maximum a posteriori probability (MAP) rule for combining the cryptographic and Bayesian detection outcome, which minimizes the average probability of detection error. We derive the probability of false alarm and missed detection and discuss the improvement of detection accuracy provided by the proposed method.