Detection of suspicious text messages and profiles using ant colony decision tree approach Online publication date: Fri, 12-Nov-2021
by Asha Kumari; Balkishan
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 19, No. 4, 2021
Abstract: The ease of human communication connectivity through short messaging services (SMS) and social networking have immensely allured the suspicious activities that menace the legitimate users. The unsolicited or uninvited messages that can lead to rumours, spam, malicious, or any other threatening activities are termed as suspicious activities. This work ensemble the attributes of the ant colony optimisation (ACO) approach with decision tree for the detection of suspicious content and profile (ACDTDSCP). In the ACDTDSCP approach, the construction of the decision tree and splitting of nodes is based on the appropriate attributes of the pheromone trail and heuristic function chosen by each ant. The research experimentation is conducted on two Twitter datasets (Social Honeypot dataset and 1KS-10KN dataset) and two SMS text corpuses (SMS Spam Collection v.1 and SMS Spam Corpus v.0.1 Big). The experimental results indicate the efficacy and potential of the proposed ACDTDSCP approach.
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