Comparative analysis of danger theory variants in measuring risk level for text spam messages

Abstract

The issue of spam has been uprising since decades ago. Impact loss from various aspects has attacked the daily life most of us. Many approaches such as policy and guidelines establishment, rules and regulations enforcement, and even anti-spam tools installation appeared to be not enough to restrain the problem. To make things even worse, the spam’s recipients still easily get enticed and lured with the spam content. Hence, an advanced medium that acts as an implicit decision maker is desperately required to assist users to obstruct their eagerness responding against spam. The simulation of spam risk assessment in this paper is purposely to give some insights of how users can identify the imminent danger of received text spam. It is demonstrated by predicting the potential hazard with three different levels of risk (high, medium and low), according to its possible impact loss. A series of simulation has been conducted to visualize this concept using Danger Theory variants of Artificial Immune Systems (AIS), namely Dendritic Cell Algorithm (DCA) and Deterministic Dendritic Cell Algorithm (dDCA). The corpus of messages from UCI Machine Learning Repository has been deployed to illustrate the analysis. The outcome of these simulations verified that dDCA has consistently outperformed DCA in precisely assessing the risk level with severity concentration value for text spam messages. The findings of this work has demonstrated the feasibility of immune theory in risk measurement that eventually assisting users in their decision making.