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TABLE OF CONTENTS
Abstract
Chapter 1 Introduction
1.2 Statement of
the problem
1.3 Objectives
1.4 Methodology
1.5 Thesis outline
Chapter 2 Background Information
2.1 Android System
Architecture
2.1.1 Linux kernel
2.1.2 Libraries
2.1.3 Android runtime
2.1.4 Application
framework
2.1.5 Applications
2.2 Dalvik Virtual
Machine
2.2.1 Hardware
constraints
2.2.2 Bytecode
2.3 Android applications
2.3.1 Application
components
2.3.2 Manifest
2.3.3 Native code
2.3.4 Distribution
2.4 Android Threat
2.4.1 Spyware
2.4.2 Root exploit
2.4.3 Botnet
2.4.4 SMS Trojans
2.4.5 Drive-by-download
2.5 Android
Security Overview
2.5.1 Permissions
2.5.2 Sandbox
2.5.3 Application
signing
2.5.4 Remote kill
switch
2.5.5 File System and
User/Group Permissions
2.5.6 Google Bouncer
2.5.7 Anti-malware
applications
2.6 Intrusion
Detection System
2.6.1 Definition
2.6.2 Detection types
Chapter 3 Related Work
3.1 Background and
Surveys
Chapter 4 Design and Implementation
4.1 Design
4.1.1 What to collect
4.1.2 Framework
design
4.1.3 Dataset
description
4.2 Implementation
4.2.1 Tools used
during implementation
Chapter 5
Experimental Result and Evaluation
5.1 Analyzing the
requested permission feature
5.2 Analyzing the
Intent information
5.3 Analyzing the
network behavior of Apps
5.4 Evaluation of
Proposed Framework using combined feature set
5.5 Performance
overhead analysis
Chapter 6
Conclusions and Recommendations
Bibliography
Appendix
Abstract
Due to the fast growing market in Android smartphone
operating systems to date cyber criminals have naturally extended their target
towards Google‘s Android mobile operating system. Threat researchers are
reporting an alarming increase of detected malware for Android from 2012 to
2013. Static analysis techniques for malware detection are based on signatures
of known malicious applications. It cannot detect new malware applications and
the attacker will get window of opportunities until the threat databases are
updated for the new malware. Malware detection techniques based on dynamic analysis
are mostly designed as a cloud based services where the user must submit the
application to know whether the application is malware or not.
As a solution to these problems, in this work we design and
implement a host based lightweight security auditing tool that suits
resource-constrained mobile devices in terms of low storage and computational
requirements. Our proposed solution utilizes the open nature of the Android
operating system and uses the public APIs provided by the Android SDK to
collect features of known-benign and known-malicious applications. The
collected features are then provided to machine learning algorithm to develop a
baseline classification model. This classification model is then used to
classify new or unknown applications either as malware or goodware and if it is
malware it alerts the user about the infection.
Our proposed solution has been tested by analyzing both
malicious and benign applications collected from different websites. The
technique used is shown to be an effective means of detecting malware and
alerting users about detection of malware, which suggests that it has the
capability to stop the spread of the attack since once the user is aware of the
malicious application he can take measures by uninstalling the application.
Experimental results show that the proposed solution has detection rate of 96.73% in RandomForest
machine learning model which is
Chapter 1
1.1 Introduction
Personal Digital Assistants (PDAs), mobile phones and
recently smartphones have evolved from simple mobile phones into sophisticated
yet compact minicomputers which can connect to a wide spectrum of networks, including
the Internet and corporate intranets. Designed as open, programmable, networked
devices, smartphones are susceptible to various malware threats such as
viruses, Trojan horses, and worms, all of which are well-known from desktop
platforms. These devices enable users to access and browse the Internet,
receive and send emails, SMSs, and MMSs, connect to other devices for
exchanging information/synchronizing, and activate various applications, which
make these devices attack targets [1].
A compromised smartphone can inflict severe damages 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 (possibly by Phishing and Social Engineering); or infect the
contacts in the phone-book. Possible attack vectors into smartphones include:
Cellular networks, Internet connections (via Wi-Fi, GPRS/EDGE or 3G network
access); USB/ActiveSync/Docking and other peripherals [1].
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