• 1 Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia
  • 2 Department of Information Systems and Cyber Security, University of Texas at San Antonio, San Antonio, Texas. United States of America
PLoS ONE, 2016;11(9):e0162627.
PMID: 27611312 DOI: 10.1371/journal.pone.0162627


To deal with the large number of malicious mobile applications (e.g. mobile malware), a number of malware detection systems have been proposed in the literature. In this paper, we propose a hybrid method to find the optimum parameters that can be used to facilitate mobile malware identification. We also present a multi agent system architecture comprising three system agents (i.e. sniffer, extraction and selection agent) to capture and manage the pcap file for data preparation phase. In our hybrid approach, we combine an adaptive neuro fuzzy inference system (ANFIS) and particle swarm optimization (PSO). Evaluations using data captured on a real-world Android device and the MalGenome dataset demonstrate the effectiveness of our approach, in comparison to two hybrid optimization methods which are differential evolution (ANFIS-DE) and ant colony optimization (ANFIS-ACO).

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.