移动操作系统漏洞：定量和定性分析（真正的EPUB）

English | 2023 | ISBN: 9781003354574 | 220 pages | True EPUB | 13.13 MB

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.

英文| 2023 |国际标准图书编号：9781003354574 | 220页|真正的EPUB | 13.13 MB 这本书对不同移动操作系统中的安全漏洞进行了深入分析。它提供了处理Android恶意软件和漏洞的方法和解决方案，并为此目的转移了机器学习和深度学习模型的最新知识。此外，它还根据不同的技术参数（如原因、严重程度、技术和软件系统类型）对软件漏洞进行了全面分析。此外，本书还介绍了软件威胁和漏洞领域的最新技术。这将有助于分析系统可能面临的各种威胁，随后，它可以指导安全工程师采取主动且具有成本效益的对策。 安全威胁呈指数级增长，对移动平台构成严重挑战。Android和iOS因其增强的功能和在用户中的受欢迎程度而突出。因此，基于安全方面对这两个移动平台进行比较非常重要。事实证明，与iOS相比，Android更容易受到攻击。恶意应用程序会造成严重的后果，如隐私泄露、应用程序崩溃、财务损失（由恶意软件触发的溢价短信造成）、任意代码安装等。因此，正如过去几年所见，Android安全是研究人员关注的主要问题。本书以结构化的形式对所有现有方法进行了详尽的回顾。 本书还重点介绍了对危害用户安全和隐私的恶意应用程序的检测，不同程序分析方法的检测性能，以及静态和动态分析过程中不同输入生成器对检测性能的影响。本书介绍了一种使用集成分类器方案检测恶意应用程序的新方法，与以前的方法相比，该方法不易受到Android生态系统和恶意软件演变的影响。该书还介绍了一种集成多级分类器方案，用于将恶意软件分类到已知的家族中。此外，我们提出了一种新的框架，利用预训练的语言模型和深度学习技术，将恶意软件映射到使用Android恶意软件行为报告利用的漏洞。然后，可以在不同的Android组件和子系统以及不同层上对映射的漏洞进行机密性、完整性和可用性评估。

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