Mobile OS Vulnerabilities; Quantitative and Qualitative Analysis by Shivi Garg & Niyati Baliyan instant download

Mobile OS Vulnerabilities; Quantitative and Qualitative Analysis by Shivi Garg & Niyati Baliyan instant download

This 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 security engineer 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 than 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 among 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 than previous methods. The book also introduces an ensemble