No abstract

No abstract

No abstract

No abstract

Over the past two decades, there has been a substantial increase in spam messages that caused critical impact loss. Besides the factor of integration of Internet and mobile technology, this issue is also due to the human's reaction towards spam. This paper presents RiCCA or Risk Concentration for Context Assessment model that performs a risk classification of text spam messages in Short Message Service (SMS) format. The identified risk levels will assist users in anticipating the potential impact of the spam message that they have been receiving. Danger Theory, a prominent theory from Artificial Immune Systems (AIS), inspires the developed model. During the simulation phase, an immunological-based testing lifecycle is applied, with the deployment of the dataset that is shared at UCI Machine Learning Repository and self-collected messages. The performance of the testing revealed a distinctive result, which more than 80% of true positive rate is achieved, employed with two variants algorithm from the Danger Theory; Dendritic Cell Algorithm (DCA) and Deterministic Dendritic Cell Algorithm (dDCA). This simulation demonstrated that the Danger Theory as a feasible model to be applied in measuring the risk of spam. The further articulation on how this immunological-based testing lifecycle is applied in computer simulation and adopting mobile spam as the case study is clarified thoroughly.