Development and Mitigation of Android Malware

Development and Mitigation of Android Malware

Vanessa N. Cooper (Kennesaw State University, USA), Hossain Shahriar (Kennesaw State University, USA) and Hisham M. Haddad (Kennesaw State University, USA)
DOI: 10.4018/978-1-4666-6324-4.ch004
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Abstract

As mobile applications are being developed at a faster pace, the security aspect of user information is being neglected. A compromised smartphone can inflict severe damage to both users and the cellular service provider. Malware on a smartphone can make the phone partially or fully unusable, cause unwanted billing, steal private information, or infect every name in a user's phonebook. A solid understanding of the characteristics of malware is the beginning step to prevent much of the unwanted consequences. This chapter is intended to provide an overview of security threats posed by Android malware. In particular, the authors focus on the characteristics commonly found in malware applications and understand the code level features that allow us to detect the malicious signatures. The authors also discuss some common defense techniques to mitigate the impact of malware applications.
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Background

In this section, we first provide an overview of the Android OS including its features and programming guidelines in Section 2.1. Section 2.2 highlights the architecture of the Android Operating Systems (OS) as well as brief discussion on security and privacy features. Section 2.3 highlights different types of malware that we consider in our discussion.

Key Terms in this Chapter

Sandbox: Detection technique that allows separately monitoring programs.

Malware: Software that implemented with a malicious intent.

Dynamic Analysis: Analysis of software in real time by program execution.

Static Analysis: Analysis of software while executing a program.

Android OS: Linux-based operating system for mobile devices.

Secure Software Architecture: Implementing a well-designed program with security in mind.

Machine Learning: Study and analysis of system data to teach computers to learn.

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