Dynamic Analysis Based Mobile Malware Classification Using Supervised Machine Learning Methods

Android based mobile malware are of great threat to the mobile users as they compromise user’s credentials without the knowledge of the user. Detection of Mobile malware and stopping their spread will save the system from further damage and misuse. Though many automated tools are available, sometimes it is very challenging to predict as malware handling methods need intelligent mechanisms. Artificial Intelligence is the promising domain which may ensure better handling of threats. Machine Learning is an important branch of AI which is expending in handling threats in an intelligent way. Intelligent intrusion detection systems are the need of the hour and machine learning methods are the most sought after and explored methods. This book titled, “Dynamic Analysis based Mobile Malware Classification using Supervised Machine Learning Methods”, discusses most of the promising Supervised Machine learning models and how they are applied for classifying Mobile Malware. This book starts from the fundamentals and gradually deals with the methodology and implementation of popular Supervised Machine Learning methods for Mobile Malware Detection. The most ideal platform Python is used, and the step-by-step methodology is presented during every phase of the implementation process. Important metrics are used to validate the performance of the ML methods and the most desirable model is recommended. Finding the most suitable model is an important phase in automating the entire process of Mobile Malware Detection. It is an ideal material for students, researchers, security professional and others who aspire to take up a serious career for the backend processing. I hope the book will be useful to the readers and many will find interesting components to explore further. My best wishes and prayers for all the readers for a great career.