DyHAP: Dynamic Hybrid ANFIS-PSO Approach for Predicting Mobile MalwareReport as inadecuate




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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.



Author: Firdaus Afifi, Nor Badrul Anuar , Shahaboddin Shamshirband, Kim-Kwang Raymond Choo

Source: http://plos.srce.hr/



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