Comprehensive review and analysis of anti-malware apps for smartphones

Abstract

The new and disruptive technologies for ensuring smartphone security are very limited and largely scattered. The available options and gaps in this research area must be analysed to provide valuable insights about the present technological environment. This work illustrates the research landscape by mapping the existing literature to a comprehensive taxonomy with four categories. The first category includes review and survey articles related to smartphone security. The second category includes papers on smartphone security solutions. The third category includes smartphone malware studies that examine the security aspects of smartphones and the threats posed by malware. The fourth category includes ranking, clustering and classification studies that classify malware based on their families or security risk levels. Several smartphone security apps have also been analysed and compared based on their mechanisms to identify their contents and distinguishing features by using several evaluation metrics and parameters. Two malware detection techniques, namely, machine-learning-based and non-machine-learning-based malware detection, are drawn from the review. The basic characteristics of this emerging field of research are discussed in the following aspects: (1) motivation behind the development of security measures for different smartphone operating system (Oss), (2) open challenges that compromise the usability and personal information of users and (3) recommendations for enhancing smartphone security. This work also reviews the functionalities and services of several anti-malware companies to fully reveal their security mechanisms, features and strategies. This work also highlights the open challenges and issues related to the evaluation and benchmarking of malware detection techniques to identify the best malware detection apps for smartphones.