DyHAP: Dynamic Hybrid ANFIS-PSO Approach for Predicting Mobile Malware.

Firdaus Afifi,Nor Badrul Anuar,Shahaboddin Shamshirband,Kim-Kwang Raymond Choo,Wen-Bo Du

doi:10.1371/journal.pone.0162627

Abstract

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).

Highlights

The ubiquity and popularity of mobile devices is likely to increase in the foreseeable future
Due to the widespread use of mobile devices and the amount of personal information stored on these devices, they have become the targets of cybercriminals such as malware authors and hackers [2,3,4,5]
Dimitrios et al [14] evaluated the suitability of five machine learning classifiers, namely: Radial Basis Function (RBF), Bayesian Networks, K-Nearest Neighbors (KNN) and Random Forest in detecting anomalies on mobile devices

Summary

Introduction

The ubiquity and popularity of mobile devices is likely to increase in the foreseeable future. A process of understanding how a particular piece of malware functions by dissecting and studying the code and its behavior with the aims of mitigating the threat [13], can be broadly categorized into static or dynamic analysis. Techniques such as machine learning have been utilized to differentiate normal and abnormal patterns in suspicious applications. Dimitrios et al [14] evaluated the suitability of five machine learning classifiers, namely: Radial Basis Function (RBF), Bayesian Networks, K-Nearest Neighbors (KNN) and Random Forest in detecting anomalies on mobile devices.

Related Work

Research Methodology

Evaluation of model performances

Conclusion

Method