Machine Learning Approach for Android Malware

Speaker:  Sanjay Misra – Lagos, Nigeria
Topic(s):  Security and Privacy

Abstract

Android is by far the most widely used mobile phone operating system around. However, Android-based applications are highly vulnerable to various types of malware attacks attributed to their open nature and high popularity in the market. The fault lies in the underneath permission model of Android applications.  These applications need a number of sensitive permissions during their installation and runtime, which enables possible security breaches by malware. Android malware prediction models are generally developed using permission-based metrics. In this session, we will present the relation between the internal structural properties with Android malware. We will introduce the basic use of various artificial intelligence (AI) techniques and feature selection (FS) methods for Android malware prediction. The focus of this session is on the Android malware prediction. In particular, we will focus on four important concepts:

(1) a framework to extract source code metrics from .apk files, (2) a framework to validate the source code metrics and identify a suitable set of source code metrics with the aim to reduce irrelevant features and improve the performance of the malware prediction model. (3) Development of Android malware prediction using different machine learning techniques and different ensemble methods. (4) A framework to evaluate the effectiveness of the developed Android malware prediction. In addition to the basic introduction and motivation, we will discuss the open research problems, important literature, proposed approach, experimental results, and future directions.

About this Lecture

Number of Slides:  40
Duration:  60 minutes
Languages Available:  English
Last Updated: 

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