This is book offers in-depth analysis of security vulnerabilities in different mobile operating systems. It provides methodology and solutions for handling Android malware and vulnerabilities and transfers the latest knowledge in machine learning and deep learning models towards this end. Further, it presents a comprehensive analysis of software vulnerabilities based on different technical parameters such as causes, severity, techniques, and software systems’ type. Moreover, the book also presents the current state of the art in the domain of software threats and vulnerabilities. This would help analyze various threats that a system could face, and subsequently, it could guide the securityengineer to take proactive and cost-effective countermeasures.
Security threats are escalating exponentially, thus posing a serious challenge to mobile platforms. Android and iOS are prominent due to their enhanced capabilities and popularity among users. Therefore, it is important to compare these two mobile platforms based on security aspects. Android proved to be more vulnerable compared to iOS. The malicious apps can cause severe repercussions such as privacy leaks, app crashes, financial losses (caused by malware triggered premium rate SMSs), arbitrary code installation, etc. Hence, Android security is a major concern amongst researchers as seen in the last few years. This book provides an exhaustive review of all the existing approaches in a structured format.
The book also focuses on the detection of malicious applications that compromise users' security and privacy, the detection performance of the different program analysis approach, and the influence of different input generators during static and dynamic analysis on detection performance. This book presents a novel method using an ensemble classifier scheme for detecting malicious applications, which is less susceptible to the evolution of the Android ecosystem and malware compared to previous methods. The book also introduces an ensemble multi-class classifier scheme to classify malware into known families. Furthermore, we propose a novel framework of mapping malware to vulnerabilities exploited using Android malware’s behavior reports leveraging pre-trained language models and deep learning techniques. The mapped vulnerabilities can then be assessed on confidentiality, integrity, and availability on different Android components and sub-systems, and different layers.
Author(s): Shivi Garg, Niyati Baliyan
Publisher: CRC Press
Year: 2023
Language: English
Pages: 702
Cover
Half Title
Title
Copyright
Contents
About the Authors
1 Introduction
1.1 Introduction
1.2 Evolution of Mobile Phones
1.3 Mobile Ecosystem and Threats
1.4 Motivation
1.5 Book Objectives
1.6 Book Organization
2 Background
2.1 Mobile Platforms
2.2 Mobile Security
2.3 Vulnerability
2.3.1 Based on the Techniques
2.3.2 Based on the Severity Levels
2.3.3 Based on the Causes of Vulnerabilities
2.3.4 Based on the Software Systems
2.4 Malware
2.4.1 Malware Attack Vectors
2.4.2 Anatomy of a Mobile Attack
2.4.3 Mobile Malware Risk Matrix
2.4.4 Malware Behavior
3 Relevant Works and Studies Related to Android and iOS
3.1 Introduction
3.2 Android versus iOS Battle
3.2.1 System Architecture
3.2.2 Security
3.2.3 Isolation Mechanism
3.2.4 Encryption Mechanism
3.2.5 App Permissions
3.2.6 Auto-Erase Mechanism
3.2.7 Application Provenance
3.3 Security Assessment of Android
3.3.1 Taxonomy Construction
3.3.2 Discussions and Future Research Directions
4 A Parallel Classifier Scheme for Vulnerability Detection in Android
4.1 Introduction
4.2 Relevant Works
4.3 Dataset Description
4.4 Proposed Methodology
4.5 System Configuration and Experimental Setup
4.6 Results
4.6.1 Individual Classifiers
4.6.2 Parallel Classifiers
4.7 Conclusion and Future Directions
5 Classification of Android Malware Using Ensemble Classifiers
5.1 Introduction
5.2 Relevant Works
5.3 Proposed Methodology
5.4 Setting Up the Data
5.5 Results
5.6 Conclusion and Future Directions
6 Text Processing–Based Malware-to-Vulnerability Mapping for Android
6.1 Introduction
6.2 Relevant Works
6.3 Malware-to-Vulnerability Mapping
6.4 Proposed Methodology
6.5 Evaluation and Results
6.6 Conclusion and Future Directions
7 Android Vulnerabilities Impact Analysis on the Confidentiality, Integrity, and Availability Triad at the Architectural Level
7.1 Introduction
7.2 Relevant Works
7.3 Design Approach
7.3.1 Vulnerability Extraction
7.3.2 Impact Analysis
7.4 Results
7.5 Conclusion and Future Directions
8 Conclusion and Future Directions
8.1 Introduction
8.2 Future Directions
Appendix A Android Malware Behavior
References
