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StackDroid: Evaluation of a Multi-level Approach for Detecting the Malware on Android Using Stacked Generalization

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1035)

Abstract

Attackers or cyber criminals are getting encouraged to develop android malware because of the rapidly growing rate of android users. To detect android malware, researchers and security specialist have been started to contribute on android malware analysis and detection related tasks using machine learning algorithms. In this paper, Stacked Generalization has been used to minimize the error rate and a multi-level architecture based approach named StackDroid has been presented and evaluated. In this experiment, Extremely Randomized Tree (ET), Random Forest (RF), Multi-Layer Perceptron (MLP) and Stochastic Gradient Descent (SGD) classifiers have been used as base classifiers in level 1 and Extreme Gradient Boosting (EGB) has been used as final predictor in level 2. It’s been found that StackDroid provides 99% of Area Under Curve (AUC), 1.67% of False Positive Rate (FPR) and 97% detection accuracy on DREBIN dataset which provides a strong basement to the development of android malware scanner.

Keywords

Android malware Machine learning Multi-level approach Stacked generalization Android malware detection 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringJahangirnagar UniversitySavar, DhakaBangladesh
  2. 2.Department of Software EngineeringDaffodil International UniversityDhakaBangladesh

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